Total 786 Information Technology job vacancies

Role Overview

Our client is looking for a skilled and motivated Data Engineer to join the Data & Analytics team. In this role, you will be responsible for designing, developing, and maintaining robust end-to-end data pipelines that power our operational and analytical capabilities.

You will work closely with data analysts, BI developers, data scientists, and business stakeholders to deliver high-quality, reliable data solutions at scale.

Key Responsibilities

Data Pipeline Engineering

• Design, develop, and maintain end-to-end data pipelines for ingesting, transforming, and delivering data from multiple source systems (databases, files, APIs, streaming platforms).

• Build and optimize ETL/ELT workflows using SQL, Python, and enterprise data integration tools.

• Ensure data pipelines are scalable, resilient, and performant to meet operational and analytical requirements.

Database & Data Platform Management

• Work hands-on with RDBMS platforms such as Oracle, DB2, SQL Server, or PostgreSQL for data extraction, transformation, and performance tuning.

• Develop and optimize SQL queries, views, and stored procedures to support reporting and analytics use cases.

• Support data modelling activities (logical and physical) for analytics and reporting layers.

Data Quality, Governance & Operations

• Implement data validation, reconciliation, and monitoring to ensure data accuracy, completeness, and consistency.

• Support operational data activities, including incident investigation, root cause analysis, and remediation.

• Maintain clear documentation for data pipelines, schemas, and operational processes to support audits and knowledge transfer.

Collaboration & Stakeholder Engagement

• Collaborate with business users, product owners, and downstream teams to gather requirements and translate them into technical solutions.

• Work closely with Data Analysts, BI developers, and Data Scientists to enable dashboards, reports, and advanced analytics.

Required Skills & Experience

• Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.

• Strong hands-on experience with SQL and relational databases (Oracle, DB2, SQL Server, PostgreSQL).

• Experience building and supporting ETL/data pipelines in enterprise environments.

• Solid understanding of data modelling, data quality, and data lifecycle management.

• Ability to troubleshoot data issues and work in production/operational environments.

• Proven experience with Python for data processing or automation.

Preferred / Nice-to-Have

• Experience with data streaming technologies such as Kafka, Apache Spark, or Apache NiFi.

• Familiarity with BI and visualisation tools such as Tableau, Qlik, or Power BI.

• Knowledge of cloud or hybrid data platforms and workflow orchestration tools (e.g. Airflow, Azure Data Factory).

. Knowledge of Agile/DevOps practices and CI/CD for data pipelines

About Innowave Tech Singapore

Innowave Tech is an Artificial Intelligence (AI) company offering solutions for the Semiconductor and Advanced Manufacturing industry. Utilizing deep industrial domain knowledge, proven experience, and innovation, we provide expert AI solutions and systems to address various industry pain points.

Roles & Responsibilities

We are seeking Senior Data Engineer to establish and lead our data infrastructure. The successful candidate will be responsible for building our data engineering practice from the ground up, implementing robust data systems for industrial AI applications, and establishing best practices that will power our semiconductor manufacturing AI solutions.

Your Role and Impact

As our first Senior Data Engineer, you will have a foundational role in building robust data infrastructure to handle manufacturing data and LLM applications, while establishing secure data practices that power our AI solutions for advanced manufacturing operations.

What You’ll Do

1. Select and manage on-premises technologies suitable for secure and efficient operations.
2. Build robust pipelines to collect, clean, and transform diverse datasets including process data, sensor data, image data, and human annotations.
3. Ensure secure, maintainable, and scalable deployment of data infrastructure.
4. Define and enforce best practices in data governance, privacy, and access control.
5. Collaboration & Deployment.

What We’re Looking For

Educational & Background:

• Minimum Poly or Bachelor Degree in Computer Science, Engineering, or a related field.
• Only Singaporean or Singapore PR can apply due to project requirement.

Technical Expertise:

• 3+ years of experience in data engineering roles, ideally with on-premises or hybrid infrastructure.
• Proven track record of building scalable data systems from ground up in a startup environment.
• Proficiency in Python and/or Java for data pipeline development.
• Solid experience with ETL frameworks (e.g., Apache Airflow, Dagster) and streaming systems (e.g., Kafka).
• Experience designing and maintaining SQL and NoSQL databases.
• Experience building and operating data lakes and data catalog.
• Familiarity with containerization (Docker), version control (Git), and CI/CD practices.

Soft Skills:

• Excellent communication skills and ability to collaborate with cross-functional technical and non-technical teams.
• Excellent problem-solving and debugging abilities.
• Ability to balance engineering tradeoffs.

Bonus Skills:

• Experience with manufacturing data systems, especially SPC, SCADA, and industrial sensor protocols (e.g., OPC UA, MQTT, Modbus).
• Familiarity with AI/ML pipelines and tools (e.g., MLflow).
• Knowledge in vector databases and LLM data infrastructure.
• Prior experience working in or with regulated industries (e.g., semiconductor, automotive, aerospace).

What we Offer

• A leading role in cutting-edge AI projects within the semiconductor industry.

• The opportunity to work with an learn from experts in the field of AI and data science.

• A dynamic, innovative, and supportive work environment.

• Competitive salary and benefits package.

• Career growth opportunities in a fast-paces technology company.

Role Summary:

We are seeking a hands-on Data Engineer to spearhead our Databricks Proof of Concept (POC) on AWS. You will be responsible for the initial environment setup, security configuration, and designing the framework for our future data platform. Working across diverse AWS and external data sources, you will build a unified, production-ready Databricks environment that validates the platform for broader adoption.

Key Responsibilities:

Platform & Infrastructure Setup

• Configure Databricks workspace integration with AWS (S3, IAM, VPC).
• Design and implement a scalable Medallion Architecture using Delta Lake (Bronze / Silver /Gold layers).
• Build automated ingestion pipelines leveraging Databricks Autoloader.

Data Engineering

• Cleanse and transform raw data from S3, RDS, and APIs into Delta tables.
• Optimize Spark jobs for performance, reliability, and cost-efficiency.
• Connect diverse AWS and external data sources into a unified Databricks environment.

Governance &Evaluation

• Establish data governance standards using Unity Catalog. (Good to have — not a hard requirement for the POC.)
• Configure Databricks Clusters with Autoscaling and Spot Instances to manage AWS spend.
• Define and evaluate POC success metrics covering performance, cost, and ease of use.
• Set up an end-to-end pipeline (Source → S3 → Databricks → BI Tool) within agreed timelines.

Qualifications and Experience:

Must-Have Skills

• Databricks Mastery — Expert knowledge of Delta Lake and Medallion Architecture (Bronze /Silver / Gold).
• PySpark / Spark SQL — Ability to write and optimize Spark code; Python strongly preferred over Scala or R.
• AWS Infrastructure — Deep understanding of S3 (bucket policies, storage), IAM(roles and policies) for secure Databricks access.
• Data Ingestion —Hands-on experience with Databricks Autoloader or Unity Catalog for managed data governance.
• 3–5+ years of professional experience in Data Engineering with PySpark / SQL.

Good to Have

• Networking — VPC configuration and AWS networking concepts.
• AWS Glue or Amazon EMR experience.
• Databricks Certified Data Engineer Professional certification.
• Experience connecting OOTB BI tools to Databricks environments.

Job Description

• Provide professional services to support the on-site build, configuration, and break-fix of Virtual Private Cloud (VPC) infrastructure in the Data Center.
• Escort engineers during on-site visits for hardware servicing and maintenance activities.
• Plan, schedule, and coordinate closely with OEM vendors for the physical installation, configuration, testing, and repair of server, storage, and network hardware.
• Ensure all hardware components—including servers, storage systems, and network devices—are mounted and installed according to design specifications.
• Follow up on cabling and patching requests to ensure proper connectivity and labeling.
• Collaborate with client teams and engineers to troubleshoot on-site hardware and infrastructure issues.
• Initiate and adhere to client change management and access control processes for all on-site activities.
• Provide physical hands-on support for server hardware, including power cycling, reboots, and component replacement.
• Other ad-hoc assigned duties.

Requirements

• Understanding of server hardware, SAN (Storage Area Network) storage, and networking components such as routers and switches.
• Proficient in server components including HDDs, CPUs, RAM, network interface cards (NICs), and storage controllers.
• Experience in Hypervisor VM Migration.
• Strong communication skills with the ability to collaborate effectively across multiple teams.
• Skilled in multitasking with a detail-oriented mindset and strong organizational abilities.
• Excellent troubleshooting skills for both hardware and network-related issues.
• Willing and able to work rotational shifts in a data center environment.
• Open to travel as required by job responsibilities.

The Data Scientist works closely with business stakeholders to uncover challenges, refine use cases, and design analytical solutions that drive measurable outcomes. This role spans the entire data science lifecycle—problem framing, data preparation, modelling, evaluation, communication of insights, and enabling adoption through compellingvisualizations and decision‑support tools.

Key Responsibilities

1. Problem Definition & StakeholderEngagement

• Facilitate discussions with business users to understand their pain points, clarify objectives, and convert them into analytically sound data science problem statements.
• Collaborate with stakeholders throughout the project to refine hypotheses, validate findings, and align on priorities.
• Manage expectations and timelines while balancing analytical depth with business needs.

2. Data Preparation & FeatureEngineering

• Conduct data cleaning, preprocessing, and transformation for both structured and unstructured data to ensure high data quality.
• Perform feature engineering to extract meaningful attributes that enhance model performance.
• Identify relevant datasets and integrate multiple data sources to support analytical work.

3. Analytical Modelling &Experimentation

• Explore data using statistical techniques to uncover trends, patterns, and relationships.
• Build machine learning models for prediction, classification, clustering, or other analytical tasks as required by the use case.
• Evaluate model performance and iterate based on stakeholder feedback and performance metrics.

4. Insights Delivery & Communication

• Translate analytical results into actionable recommendations that stakeholders can apply to drive decisions or business improvements.
• Develop clear, compelling narratives that explain insights and model behaviours in an accessible manner.
• Present analysis with well‑designed visuals, dashboards, and interactive tools that support storytelling and data exploration.

5. Visualisation & User‑Facing Tools

• Design dashboards and interactive visualizations to facilitate self‑service analytics and real‑time data monitoring.
• Apply strong visualization principles to ensure insights are intuitive and impactful.
• Build and deploy customised visual interfaces tightly integrated with underlying data systems.

6. Deployment, Monitoring &Continuous Improvement

• Support model deployment into production environments and collaborate with engineering teams to operationalize models.
• Monitor model performance over time and recommend enhancements as required.
• Contribute to agile project workstreams and participate in ceremonies such as stand‑ups, reviews, and retrospectives.

Required Skills & Competencies

Analytical & Technical Skills

• Ability to convert business challenges into analytical questions and identify appropriate data sources.
• Proficiency in writing data preparation and analysis scripts using tools such as Python, R, SQL, pandas, or equivalent.
• Strong foundation in descriptive statistics, probability, data exploration, and hypothesis testing.
• Skilled in building and validating machine learning models, using modern techniques and frameworks.
• Solid understanding of system design concepts, data structures, and algorithms.

Data Visualization & Storytelling

• Strong command of data visualization principles and tools.
• Capable of developing dashboards and interactive visual tools (e.g., using Tableau, Power BI, Plotly, or custom frameworks).
• Ability to communicate analytical insights through structured storytelling tailored for decision‑makers.

Data Engineering Familiarity

• Understanding of data modelling, data access patterns, and data storage structures such as data lakes, data marts, and data warehouses.
• Familiarity with REST APIs, web protocols, and data extraction via web scraping technologies.
• Comfortable working with big data frameworks (e.g., Spark, Hadoop, Kafka) when required for large-scale processing.

Soft Skills

• Strong communication skills, both written and verbal, with the ability to influence non‑technical audiences.
• Effective stakeholder management and experience working in iterative cycles with feedback loops.
• Organized, self-driven, and able to manage multiple priorities in an agile environment.

Experience Requirements

• Proven experience conducting end‑to‑end data science projects—from scoping and data preparation to modelling and deployment.
• Experience collaborating with diverse teams and managing stakeholders across business and technical functions.
• Prior experience in agile project management or participating in agile delivery teams.

Qualifications

• Degree or equivalent experience in Data Science, Statistics, Computer Science, Engineering, Mathematics, Information Systems, or related fields.
• Experience with production‑grade machine learning workflows is an advantage.

Please send your details in MS Word format to john@oaktree.com.sg:

- Position applying for

- Current remuneration

- Expected remuneration

- Notice period

John Goh Meng Chye

EA License No : 06C4642

EA Reg No : R1102621

We regret that only shortlisted candidates will be notified.

Avensys is a reputed global IT professional services company headquartered in Singapore. Our service spectrum includes enterprise solution consulting, business intelligence, business process automation and managed services. Given our decade of success, we have evolved to become one of the top trusted providers in Singapore and service a client base across banking and financial services, insurance, information technology, healthcare, retail and supply chain.

We are currently looking to hire a Data Platform Engineer. This is an exciting opportunity to expand your skill set, achieve job satisfaction and work-life balance. More details as below.

ROLES AND RESPONSIBILITIES

• Design, develop, and maintained to end data pipelines for ingesting, transforming, and delivering data from multiple source systems (databases, files, APIs, streaming platforms).
• Build and optimize ETL / ELT workflows using SQL, Python, and enterprise data integration tools.
• Ensure data pipelines are scalable, resilient, and performant to meet operational and analytical requirements.
• Database & Data Platform Management
• Work hands on with RDBMS platforms such as Oracle, DB2, SQL Server, orPostgreSQL for data extraction, transformation, and performance tuning.
• Develop and optimize SQL queries, views, and stored procedures to supportreporting and analytics use cases.
• Support data modelling activities (logical and physical) for analytics and reporting layers.
• Data Quality, Governance & Operations
• Implement data validation, reconciliation, and monitoring to ensure data accuracy, completeness, and consistency.
• Support operational data activities, including incident investigation, root cause analysis, and remediation.
• Maintain clear documentation for data pipelines, schemas, and operational processes to support audits and knowledge transfer.
• Collaboration & Stakeholder Engagement
• Collaborate with business users, product owners, and downstream teams to gather requirements and translate them into technical solutions.
• Work closely with Data Analysts, BI developers, and Data Scientists to enable dashboards, reports, and advanced analytics.
• Participate in Agile ceremonies and contribute to sprint planning, estimation, and delivery

REQUIREMENTS

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
• Strong hands on experience with SQL and relational databases (Oracle, DB2,SQL Server, PostgreSQL).
• Experience building and supporting ETL / data pipelines in enterprise environments.
• Solid understanding of data modelling, data quality, and data lifecycle management.
• Ability to troubleshoot data issues and work in production / operational environments.
• Experience with Python for data processing or automation.
• Experience with data streaming technologies (e.g. Kafka, Spark, NiFi).
• Experience with BI and visualization tools such as Tableau, Qlik, or PowerBI.
• Knowledge of cloud or hybrid data platforms and orchestration tools.
• Knowledge of Agile / DevOps practices and CI/CD for data pipelines.

WHAT’S ON OFFER

You will be remunerated with an excellent base salary and entitled to attractive company benefits. Additionally, you will get the opportunity to enjoy a fun and collaborative work environment, alongside a strong career progression.

To submit your application, please apply online or email your UPDATED CV in Microsoft Word format to keerthana@aven-sys.com. Your interest will be treated with strict confidentiality.

CONSULTANT DETAILS

Consultant Name: Keerthana Ramakrishnan
Avensys Consulting Pte Ltd
EA License 12C5759

Privacy Statement: Data collected will be used for recruitment purposes only. Personal data provided will be used strictly in accordance with the relevant data protection law and Avensys' privacy policy.

Data engineer (Azure) – Synapse and Pyspark, Python, Datawarehouse and Azure Data Explorer, Azure Devops

Job Scope

• Design, review and development of Pyspark scripts. Testing, troubleshooting of data pipelines, orchestration.

• Designing and developing reports and dashboards in PowerBI,setting up access control with row level security DAX query experience.

• Establishing connections to source data systems such as on-prem databases, IOT devices, APIs.

• Managing the collected data in appropriate storage/data-base solutions e.g. file systems, SQL servers, Big Data platforms such as Hadoop, HANA, etc. as required by the specific project requirements.

• Design, development of relevant data pipelines using pyspark, copy data activities for batch ingestion.

• Performing data integration e.g. using database table joins, or other mechanisms at an appropriate level as required by the analysis requirements of the project.

• Deployment of pipeline artifacts from one environment to the other using Azure Devops.

Skills & Experience

• Bachelor’s Degree in Computer Science or Engineering with 2 years of experience in Azure Data engineering, Python, Pyspark or Big Data development.

• Sound Knowledge of Azure Synapse analytics for pipelines, orchestration, set up.

• 1-2 experience in Visualization design and development with Power BI. Knowledge on row-level security, access control.

• Sound experience in SQL, Datawarehouse, data marts, data ingestion with Pyspark and Python.

• Expertise in developing and maintaining ETL processing pipelines in cloud-based platforms such as AWS, Azure, etc. (Azure Synapse or data factory preferred)

• Team player with good interpersonal, communication, and problem-solving skills.

• Preferred to have Devops expertise.

Working hours :

8:30am to 6pm (Monday to Friday) onsite, no hybrid option.

• Minimum of Bachelor’s Degree in Computer Science, Computer Engineering, Machine Learning / Data Science / AI or related disciplines;
• Able to understand and apply a range of AI/ML  techniques for regression and classification
• Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost, NLTK, spaCy )
• Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g. RAG)
• Experience with context engineering techniques and prompt optimization strategies
• Proficient in git, SQL
• Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy)

Bonus skillsets:

• Proficient in modern programming languages (e.g. typescript, C#)
• Experience in cloud platform and services preferably in AWS
• Experience in Docker/Kubernetes
• Experience with web frameworks and full-stack development, including backend frameworks (e.g. FastAPI, Flask, Express.js) and RESTful API development, and frontend technologies (e.g. React, Vue.js, HTML/CSS, TypeScript)
• Experience with LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face Transformers)
• Knowledge of vector databases and embedding techniques (e.g. Pinecone, Chroma, FAISS)
• Understanding of AI agent frameworks and multi-agent systems
• Strong presentation skills and ability to explain technical concepts clearly to a non-technical audience
• Proficient in statistical software tools (e.g. R, SAS)

Product area

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. 

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Job description

Our thirst for technology is a part of everything we do. The Data Center Engineering team takes the physical design of our data centers into the future. Our lab mirrors a research and development department -- cutting-edge strategies are born, tested and tested again. Along with a team of great minds, you take on complex topics like how we use power or how to run state-of-the-art, environmentally-friendly facilities. You're a visionary who optimizes for efficiencies and never stops seeking improvements -- even small changes that can make a huge impact. You generate ideas, communicate recommendations to senior-level executives and drive implementation alongside facilities technicians.

Additional job description

The Data Center Design Integration team is a multi disciplinary team of architects and Information and Communication Technology (ICT)/Telecommunications (Telecom) designers that work on next generation data center designs. We are a sub-team of the broader Data Center Technology and Systems (DCTS) organization.

As a Telecom Lead, you will be responsible for working with executive level engineers across all disciplines to develop integrated telecom communication designs at the product and top level assembly state of a data center. You will lead external consultants to develop coordinated construction level drawing packages for manufacturing and construction.

Qualifications

Job responsibilities

• Work with internal telecom engineers and network designers to rationalize requirements into products, conceptual one lines and conceptual rack or tray layouts to enable adequate telecom infrastructure.
• Work with cross-functional disciplines across architecture, civil, mechanical, electrical, controls, security on product development, and assembly integration of telecom infrastructure.
• Participate in internal and external ICT/telecom product and data center design reviews across the following disciplines: Telecom, Security, Controls and Networking.
• Review construction level drawings produced by internal and external vendors that document ICT/telecom scope for program reference.
• Review and enable best practices for converged network allocations across all mechanical, electrical, and controls products. Act as an escalation path for site localization of canonical design where local requirements necessitate engineering judgement on adjustments to ICT/telecom design.

Minimum qualifications

• Bachelor's degree in Mechanical Engineering, Electrical Engineering or IT Engineering, or equivalent practical experience.
• 5 years of experience working with external telecom vendors on telecom products.

Preferred qualifications

• Master's degree in Mechanical or Electrical or IT engineering.
• 6 years of experience as a Registered Communications Distribution Designer (RCDD) Certified professional with ICT/telecom scope within mechanical and electrical data center products.
• 6 years of experience as key contributor on ICT/telecom scope within mechanical and electrical data center products
• Experience with Autodesk Revit or other 3D modeling software developing telecom rack and tray layouts.
• Ability to travel to visit data center sites or manufacturing partners.

About Us

• Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.​

Why Join ByteDance

• Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life - a mission we work towards every day.​

• As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.​

Diversity & Inclusion​

• ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.​

Job highlights

• Positive team atmosphere, Paid leave, Meals provided

Team Introduction:

• Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively.
• The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls. We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion.
• We are looking for data scientists and AI developers who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals.
• You will be deploying your engineering, data analytics and data science skills to be part of the mission to build state-of-the-art analytics products for the audit team.

Responsibilities:

• Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk-management standards.
• Risk Identification & Mitigation: Identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.
• Measurement Metrics & Statistical Validation: Define and assess model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
• Communication & Collaboration: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals.
• Clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
• Data Analytics Services: Partner with auditors to provide data support and guidance for audit engagements, including conducting interviews, observing systems and operations, developing queries and testing strategies, deploying data quality checks to ensure completeness and accuracy for data sets, and deriving insights.
• Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data partners to maintain completeness and accuracy of these assets.
• Automation and self-service analytics: partner with auditors to identify and analyze key risk indicators, contribute to a continuous auditing data strategy that will translate into various use cases and corresponding data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines, as well as dashboards to support the solutions.
• AI-Driven Automation and Insights: Leverage machine learning and AI to automate business and audit processes, surface insights from unstructured and structured data, and extend the team’s ability to deliver actionable recommendations at scale. Develop, train, and implement proprietary machine learning and AI models, to scale up audit testing insights.
• Professional Development: Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and ByteDance products through continuous education. Provide data training to empower the audit team to derive insights.

Qualifications

Minimum Qualifications:

• Bachelor's degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.
• Minimum of 5 years professional experience in applied data science, machine learning engineering, or AI research, specifically working with LLMs and traditional ML models and at least 5 years practical experience of data science or analytics from the technology sector, including but not limited to B2C SaaS, media tech, ecommerce, social media platforms, fintech etc.
• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLMAuditor (probe generation/answering cycles, human-in-the-loop assessments).
• Hands-on experience in designing, deploying, and monitoring large-scale ML models with thorough understanding of lifecycle risks and controls plus strong proficiency in SQL and Python (including libraries such as Hugging Face Transformers, TensorFlow, PyTorch, scikit-learn), data analysis tools, and ML pipeline orchestration platforms.
• Expertise in defining and assessing model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
• Extensive knowledge of transformer-based LLM architectures (e.g., GPT, BERT, T5, PaLM) and classical ML algorithms (e.g., regression, tree-based methods, neural networks).
• Working knowledge of classical ML algorithms and LLM architecture and deep technical expertise in LLMs and Traditional ML and a proven track record supporting or performing AI/ML model audits or evaluations within a corporate, regulatory, or advisory context.

Preferred Qualifications:

• PHD degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.
• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance.
• Familiarity with emerging LLM auditing methodologies such as LLM Auditor (probe generation/answering cycles, human-in-the-loop assessments).
• Ability to analyze model design, training methods, data pipelines, and inference behaviors.
• Capability to identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and to recommend remediation strategies.
• Experience building and maintaining data analytics solutions for continuous audit programs, including automating common analyses and recurring checks plus the ability to clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
• Experience with data integration, ETL processes, and large-scale data processing systems plus working knowledge of cloud-based infrastructure such as AWS, GCP, Azure or Snowflake; working knowledge of large scale data processing techniques, such as Hadoop, Flink and MapReduce and a good understanding of data warehouse and data modeling principles.
• Front end and back end software development skills.

Responsibilities

• Design, develop, and maintain end to end data pipelines for ingesting, transforming, and delivering data from multiple source systems (databases, files, APIs, streaming platforms).

• Build and optimize ETL / ELT workflows using SQL, Python, and enterprise data integration tools.

• Ensure data pipelines are scalable, resilient, and performant to meet operational and analytical requirements.

Database & Data Platform Management

• Work hands on with RDBMS platforms such as Oracle, DB2, SQL Server, or PostgreSQL for data extraction, transformation, and performance tuning.

• Develop and optimize SQL queries, views, and stored procedures to support reporting and analytics use cases.

• Support data modelling activities (logical and physical) for analytics and reporting layers.

Data Quality, Governance & Operations

• Implement data validation, reconciliation, and monitoring to ensure data accuracy, completeness, and consistency.

• Support operational data activities, including incident investigation, root cause analysis, and remediation.

• Maintain clear documentation for data pipelines, schemas, and operational processes to support audits and knowledge transfer.

Collaboration & Stakeholder Engagement

• Collaborate with business users, product owners, and downstream teams to gather requirements and translate them into technical solutions.

• Work closely with Data Analysts, BI developers, and Data Scientists to enable dashboards, reports, and advanced analytics.

• Participate in Agile ceremonies and contribute to sprint planning, estimation, and delivery

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.

• Strong hands on experience with SQL and relational databases (Oracle, DB2, SQL Server, PostgreSQL).

• Experience building and supporting ETL / data pipelines in enterprise environments.

• Solid understanding of data modelling, data quality, and data lifecycle management.

• Ability to troubleshoot data issues and work in production / operational environments.

• Experience with Python for data processing or automation.

• Experience with data streaming technologies (e.g. Kafka, Spark, NiFi).

• Experience with BI and visualization tools such as Tableau, Qlik, or Power BI.

• Knowledge of cloud or hybrid data platforms and orchestration tools.

• Knowledge of Agile / DevOps practices and CI/CD for data pipelines.

Licence no: 12C6060

Defectivity Data Analytics Engineer

We are seeking a skilled Data Analytics Engineer to join our Defectivity Engineering team in the wafer fabrication fab. The ideal candidate will leverage data analytics, statistical methods, and engineering knowledge to identify, analyze, and reduce wafer defects, improving yield and process quality.

The Job

• The Defectivity department is a Site function, meaning that you will have the opportunity to work closely with both Defectivity Fab SG6/8 and Fab SG8E.
• Develop and implement data-driven models and algorithms to detect defect patterns and root causes.
• Collaborate with process engineers, equipment engineers, and quality teams to translate data insights into actionable process improvements.
• Support the integration of advanced analytics and machine learning techniques for predictive defect detection.
• Participate in cross-functional problem-solving teams to address complex defectivity challenges.
• Ensure compliance with fab safety, quality, and documentation standards.

Requirements

• Bachelor’s or Master’s degree in Engineering, Data Science, Statistics, or related field.
• Strong experience in data analytics, preferably in semiconductor manufacturing or wafer fabrication.
• Proficiency in programming languages such as Python, R, or SQL.
• Familiarity with statistical software and data visualization tools (e.g., Spotfire, Power BI).
• Understanding of semiconductor fabrication processes and defectivity mechanisms.
• Excellent problem-solving, communication, and teamwork skills.
• Ability to work in a fast-paced, dynamic manufacturing environment.

Analytics Center of Excellence (COE) at Income Insurance is the enterprise team that champions and develops AI/ML solutions. We focus on in-house solution development as well as partnerships. To ensure the successful execution of Income’s enterprise strategy to build Analytics as a product through impactful AI/ML solutions, driving innovation, and cultivating AI culture, we are seeking a highly skilled and experienced Data Scientist to join our team. The selected candidate will focus on building Generative AI-based solutions

Responsibilities:

• Take ownership of AI/ML use cases, from design and implementation to continuous enhancement.
• Act as a technical specialist in AI, primarily focusing on Generative AI. Design and develop solutions to address various business challenges, particularly those related to optimizing operational efficiency.
• Collaborate with other data engineers, analysts, data scientists, product specialists, and stakeholders to build well-crafted, pragmatic, and robust solutions that meet business requirements.
• Stakeholder management and engagement: proactively engage with stakeholders to understand their needs and translate them into technical requirements for AI/ML modeling.
• Maintain documentation of dataset curation, modeling approach, model performance, code changes, and workflows.
• Demonstrate a strong understanding of data privacy regulations such as PDPA, and AI governance guidelines to ensure compliance.
• Foster an innovative and growth-oriented mindset, continuously seeking opportunities to enhance AI/ML models and drive improvements across the organization.

Requirements:

• 3–5 years of experience in a data science role, with demonstrable expertise in the AWS platform. Experience with Microsoft Copilot Studio is a plus.
• Bachelor’s degree in computer science or equivalent.
• Familiarity with techniques for Document Chunking, Embedding, and Information Retrieval for improving model accuracy and relevance.
• Expertise in Prompt Engineering for designing and managing effective prompts.
• Hands-on experience with AWS Bedrock, including deploying solutions using foundation models and integrating them into scalable applications using APIs and orchestration tools.
• Experience in Agentic Workflow for maximizing the utility of models, including understanding user intent and context to drive meaningful interactions.
• In-depth knowledge of supervised and unsupervised ML models – linear & logistic regression, clustering, tree-based models like random forest, bagging, and boosting models.
• Proficient in SQL, Python, and Spark.
• Proven experience in implementing MLOps practices on AWS.
• Familiar with Data Warehouses such as Redshift, Hive, and S3.
• Passionate about technology and always looking to upskill based on new developments in the AI space.
• AWS or Microsoft certifications will be a plus.
• Experience in the financial industry, telecommunications, or consulting is preferred

Role Summary

The Data Center Operations & Customer Service Engineerprovides operational and technical support for production infrastructure andcustomer services. The role performs hands-on data center operations andsupports troubleshooting activities involving servers, networking equipment,and data center infrastructure.

Key Responsibilities

·      Respond to customer incidents and service requests according to SLA requirements.

·      Create and manage operational tickets and coordinate with engineering teams.

·      Perform routine data center inspections including servers, racks, cabling, and networking equipment.

·      Execute operational procedures such as rack/stack, cabling, and hardware component replacement.

·      Assist engineers with troubleshooting by collecting logs, hardware indicators, and diagnostic information.

·      Support monitoring systems and participate in incident response processes.

Required Qualifications

·      Bachelor’s degree in IT, Computer Science, Engineering, or equivalent practical experience.

·      2–5 years of experience in data center operations or infrastructure operations.

·      Hands-on experience with servers, networking equipment, and data center environments.

·      Basic Linux system administration and operational experience.

·      Understanding of networking fundamentals such as TCP/IP and switching.

·      Willingness and ability to work in a shift-based support model, including night shifts, weekends, and public holidays, in accordance with business operational needs and in compliance with MOM regulations.

Preferred Qualifications

·    Hands-on experience performing rack/stack, cabling, and hardware maintenance in data centers.

·      Experience troubleshooting server and network equipment.

·      Familiarity with monitoring systems and incident management tools.

·      Experience supporting large-scale infrastructure environments.

Main responsibilities:

• Provide day-to-day support for business applications and databases, resolving application bugs, database errors, and integrate new features in a timely manner
• Translate business rules and process needs into structured data requirements and system specifications
• Build and refine database frameworks to deliver accurate reporting and effective data extraction
• Develop conceptual, logical, and physical data models
• Assist with the implementation of application patches, upgrades, and database migrations
• Support and manage user access to applications and databases
• Conduct system and application tests to ensure functionality and performance before new releases or updates
• Monitor database performance, identify bottlenecks, and apply tuning measures to optimize performance
• In-charge of IT audits and BCP
• Ensure compliance with IT policies, security standards, and best practices
• Backup of IT system support tasks
• Other assigned duties

Requirements

• Diploma or Degree in Information Technology, Computer Science or related field
• 5 years experience in application and database system role
• Familiar with SICS and SAP Business Intelligent, highly preferred
• Good knowledge of MS SQL database and scripting languages
• Strong knowledge of database design, data modeling, and query optimization
• Good technical, analytical and problem-solving skills
• Excellent interpersonal and communication
• Ability to work independently and as part of a team

Interested applicants please send your resume to venessagoh@recruitexpress.com.sg

Venessa Goh Wee Ni

R24124686

Recruit Express Pte Ltd

EA License No: 99C4599

We regret that only shortlisted candidates will be contacted.

Key Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.

• Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.

• Develop and optimize ETL/ELT processes using tools like Databricks, Spark.

• Ensure data quality, versioning, and lineage across the ML lifecycle.

• Support the deployment and monitoring of AI models in production environments.

• Translate business requirements into technical solutions, working closely with cross-functional teams.

• Contribute to the development of data services and APIs that power intelligent decision-making tools.

Functional Knowledge

• Masters Degree in Data Science, Computing, AI/ Analytics

• 7-10 years of expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).

• Experience with cloud-based data platforms (Azure).

• Experience with LLM and Agentic AI

• Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.

• Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.

• Understands the role of data in driving supply chain and planning decisions.

• Able to align data engineering solutions with business KPIs and operational goals.

• May lead technical workstreams or mentor other engineers.

• Takes ownership of data infrastructure components and contributes to architectural planning.

• Solves complex data challenges with creativity and precision.

• Proactively identifies and resolves data quality, performance, and scalability issues.

• Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.

• Supports real-time and predictive decision-making across global operations

• Communicates effectively with data scientists, AI engineers, and business stakeholders.

• Comfortable working in a fast-paced, collaborative environment with evolving priorities.

Overview

• Analyze the current data landscape, client capabilities, and business requirements to design scalable, high‑performance, and highly available data solutions.
• Lead cross‑functional teams in designing and developing data solution configurations aligned with the proposed architecture.
• Recommend best‑fit approaches to address development, integration, and performance challenges in data warehouse and business intelligence environments.
• Define strategic direction and create implementation roadmaps for enterprise data initiatives.
• Serve as the primary point of contact for architecture, design, and development‑related queries.
• Apply deep expertise in both modern and traditional data technologies to guide architectural decisions.
• Identify, evaluate, and adopt emerging technologies and methodologies to improve performance, automation, and scalability.
• Support business development by shaping solution architectures for proposals and participating in client meetings with the sales team.

Experience and Qualifications

• 15+ years of experience in data engagements, leading data strategy, roadmaps, and end‑to‑end implementation programs.
• Expertise in enterprise data strategy, alignment with business objectives, transformation initiatives, and warehouse architecture models.
• Strong understanding of information flow, integration and interfacing approaches, business information models, logical and canonical data models, and data lifecycle management.
• Experience with analytical, regulatory, and compliance reporting models.
• Mandatory banking industry experience with strong domain expertise.
• Extensive experience in data migration strategy, planning, costing, execution, retention, archival, and disposal.
• Master Data Management (MDM) across multi‑domain environments using tools such as Informatica MDM, Stibo PIM, and Talend Data Fabric.
• Data governance capability assessment (as‑is/to‑be), framework design, model development, roadmap creation, and execution.
• Proficiency with architecture tools such as PowerDesigner, ArchiMate, Toad, and ERWin.
• Strong foundation in normalized/denormalized design, ER modeling, star schema design, hierarchical models, and Inmon and Kimball methodologies.
• Hands‑on experience with platforms including Postgres, Oracle, SQL Server, DB2, Teradata, Hive, Pig, Impala, MongoDB, Cassandra, HBase, and Snowflake.

Additional Skills

• Programming and scripting: MS SQL, PL/SQL, NoSQL, Shell scripting, VBA, T‑SQL, DAX.
• Reporting and UI tools: MS SSRS, Oracle Forms (6i, 9i, 10g), Intellimatch, MS Access.
• RDBMS experience: Oracle 9i/10g, MS SQL Server 2008–2014, Oracle 9i AS/10g.
• Web and cloud technologies: Sitecore, AWS, WordPress, Google Analytics/GTM.
• Management tools: MS Project, MS Office Suite, JIRA, rLean.
• Frameworks and certifications: TOGAF, DAMA‑DMBOK.

We are seeking a Data Architect to own the end-to-end data architecture for operational and analytics data within data centre environments. This role spans industrial data ingestion, data modeling, analytics enablement, and embedded data governance, ensuring OT data (e.g., OPC UA, BMS, PQMS) is transformed from raw telemetry into trusted, business-ready insights.

This is a hands-on architecture role responsible for defining standards, building critical components of the data platform, and ensuring operational reliability. The role serves as the technical lead for OT data ingestion, analytics architecture, and embedded data governance.

Make an impact by 

1. OT Data Engineering & Platform Architecture

• Design, build, and operate OPC UA-based data ingestion pipelines from BMS, PQMS, PLCs, and sensors.
• Implement edge and on-premises data pipelines suitable for data centre environments.
• Manage raw and curated data layers, ensuring reliability, consistency, and performance.
• Address time-series data challenges, including sampling rates, timestamps, aggregation strategies, and late-arriving data.
• Monitor, troubleshoot, and optimise production pipelines.

2. End-to-End Architecture Ownership

• Own and evolve the end-to-end data architecture from OT source systems to analytics consumption.
• Define and standardise:
• OPC UA connectivity and subscription patterns
• Streaming versus batch ingestion strategies
• Buffering, retry, and fault-tolerance mechanisms
• Establish architectural standards for:
• Time-series schemas
• Asset and tag hierarchies
• Naming conventions and metadata structures
• Define and uphold non-functional requirements across the platform, including:
• Availability and resilience
• Latency and performance
• Scalability
• Security at the OT/IT boundary
• Provide technical leadership and guidance on data architecture and design decisions.

3. Analytics Architecture & Enablement

• Transform curated OT data into analytics-ready fact and dimension models.
• Design and maintain data marts and datasets for dashboards and reporting.
• Define and govern the analytics and semantic layer to enable consistent KPI usage.
• Establish standards for metric calculation logic, grain definition, time windows, and aggregation rules.
• Ensure a single source of truth for business metrics and minimise duplication.
• Enable self-service analytics through well-documented and trusted datasets.

4. Data Governance, Quality & Lineage

• Embed data governance into pipelines and analytics models, including:
• Clear data ownership and domain attribution
• Technical metadata capture (tags, units, frequency, source)
• Define and enforce data quality rules (completeness, validity, timeliness).
• Ensure end-to-end lineage and traceability from OT source systems to business KPIs.
• Apply access controls and data security policies aligned with OT and enterprise standards.
• Maintain documentation to support auditability and transparency.
• Collaborate with stakeholders to ensure data is fit for purpose.

5. Collaboration & Continuous Improvement

• Partner with data analysts and stakeholders to translate business requirements into scalable analytics solutions.
• Validate analytics outputs against business intent and operational context.
• Act as a technical advisor on data usage, constraints, and interpretation.
• Drive continuous improvement of the data platform and analytics ecosystem.

Required Skills & Experience

• Extensive experience in data architecture, data engineering, analytics engineering, or industrial data platforms (typically gained over multiple years of progressive responsibility).
• Strong hands-on experience with OPC UA (clients, servers, security, certificates, subscriptions).
• Experience with BMS, PQMS, SCADA, or industrial telemetry systems.
• Strong programming skills in Python and proficiency in SQL.
• Experience with streaming and messaging technologies (e.g., Kafka, MQTT, or equivalent).
• Solid understanding of time-series data modeling.
• Experience working in on-premises or data centre environments.
• Hands-on experience with data quality management, lineage and metadata management, and metric governance or semantic modeling.
• Ability to balance architecture, delivery, and operational responsibilities.

Nice to Have

• Experience with hybrid cloud and on-premises data architectures.
• Experience in energy, facilities, or data centre operations.
• Exposure to analytics or machine learning use cases on operational data.
• Experience defining enterprise KPIs or analytics standards.

Key Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.

• Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.

• Develop and optimize ETL/ELT processes using tools like Databricks, Spark.

• Ensure data quality, versioning, and lineage across the ML lifecycle.

• Support the deployment and monitoring of AI models in production environments.

• Translate business requirements into technical solutions, working closely with cross-functional teams.

• Contribute to the development of data services and APIs that power intelligent decision-making tools.

Functional Knowledge

• Masters Degree in Data Science, Computing, AI/ Analytics

• 7-10 years of expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).

• Experience with cloud-based data platforms (Azure).

• Experience with LLM and Agentic AI

• Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.

• Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.

• Understands the role of data in driving supply chain and planning decisions.

• Able to align data engineering solutions with business KPIs and operational goals.

• May lead technical workstreams or mentor other engineers.

• Takes ownership of data infrastructure components and contributes to architectural planning.

• Solves complex data challenges with creativity and precision.

• Proactively identifies and resolves data quality, performance, and scalability issues.

• Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.

• Supports real-time and predictive decision-making across global operations

• Communicates effectively with data scientists, AI engineers, and business stakeholders.

• Comfortable working in a fast-paced, collaborative environment with evolving priorities.

We are seeking a dynamic and strategic Data Engineer to design, build, and scale enterprise-grade data platforms and pipelines for the product.

You will play a key role in shaping the product’s data strategy, ensuring high standards in data architecture, performance, scalability, and governance. Beyond core data engineering, you will drive innovation in AI-enabled capabilities, leveraging data as a foundation to unlock insights, improve decision-making, and enhance user outcomes across the public service.

This role requires strong technical expertise, analytical thinking, and stakeholder engagement skills to collaborate across product, engineering, policy, and business teams to deliver impactful, data-driven solutions.

Decision-making

· When making important decisions, we identify overarching goals, weigh up our options, identify impact, and clearly communicate and manage the risks and trade-offs of our decision.

Ownership

· In addition to technical responsibilities, this means having opinions on what is being done and having ideas on what should be done next. Building something that you believe in is the best way to build something good.

Continuous Learning

· Working on new ideas often means not fully understanding what you are working on. Taking time to learn new architectures, frameworks, technologies, and even languages is not just encouraged but essential.

Our Team

Our team is made up of passionate individuals committed to solving challenges and driving meaningful change. We embrace diversity of thought, encourage experimentation, and foster a culture of continuous learning. As part of this team, you will collaborate with Business Owner, Product Director, Product Managers, UX Designers, Engineers and Business Users.

Data Strategy, Engineering & Product Delivery

· Engage stakeholders to understand business challenges, refine use cases, and identify opportunities to harness data for product and policy decisions.

· Design, build, and maintain scalable data pipelines, data platforms, and reusable datasets to support analytics and product use cases.

· Develop data models and architectures to support complex business processes and reporting needs.

· Implement ETL/ELT processes to ingest, transform, and serve structured and unstructured data.

· Design and deliver analytics workflows, dashboards, and data products to enable insights and decision-making.

· Work iteratively with product teams to validate insights, refine analyses, and deliver measurable outcomes.

AI, Machine Learning & Advanced Analytics

· Develop and apply machine learning and Large Language Model (LLM)-based solutions, including prompt engineering and AI-enabled applications where relevant.

· Operationalise models through MLOps practices, including deployment, monitoring, and lifecycle management.

· Stay abreast of emerging trends in AI, data architectures, and optimisation techniques, and apply them to enhance product capabilities.

· Ensure responsible AI practices, including considerations for safety, fairness, and robustness.

Data Platforms, Integration & DataOps

· Design and implement moderately complex data systems and platforms, including supporting infrastructure and integrations.

· Apply advanced data integration techniques (e.g. streaming, Change Data Capture, message queues) to enable real-time and batch processing.

· Champion DataOps practices such as automated pipeline deployments, monitoring, and data observability.

· Ensure data quality, reliability, and integrity across systems and pipelines.

· Proactively manage and reduce technical debt to ensure long-term system stability.

Governance, Risk & Compliance

· Navigate data privacy, governance, and regulatory requirements, ensuring compliance with policies and standards.

· Identify risks in data systems and proactively address potential failure points.

· Align system design with security, performance, and compliance best practices across GovTech platforms.

Leadership, Collaboration & Delivery

· Own and deliver complex data engineering assignments across the full data lifecycle with minimal supervision.

· Break down complex problems into actionable tasks and coordinate delivery across teams.

· Drive high-quality, scalable delivery through engineering excellence and best practices.

· Mentor junior engineers and guide peers through code reviews, architecture discussions, and technical problem-solving.

· Collaborate effectively with Product Managers, Engineers, Designers, and Business stakeholders to deliver integrated solutions.

Strategic Alignment & Organisational Impact

· Contribute to team strategy, balancing short-term delivery with long-term platform sustainability.

· Evaluate new technologies and communicate trade-offs to technical and non-technical stakeholders.

· Participate in cross-team initiatives and align solutions with broader organisational goals.

Experience & Education

· Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.

· At least 5 years of experience in data engineering.

· Proven experience delivering data platforms or products in large-scale, enterprise environments.

Technical Skills

· Strong expertise in data modelling (OLTP, OLAP, dimensional modelling).

· Experience with cloud data platforms and tools (e.g. AWS, Azure Synapse/Microsoft Fabric, Snowflake, Databricks, Redshift, Data Lakes).

· Proficiency in big data technologies (e.g. Hadoop, Spark, Kafka, Flink).

· Strong programming skills in Python, Scala, or Java.

· Experience with CI/CD and DataOps practices (e.g. SHIP-HATS or equivalent).

· Expertise in data integration techniques (e.g. ETL/ELT, streaming, APIs).

AI & Machine Learning (Advantageous)

· Understanding of machine learning and LLM concepts, including prompt engineering and AI application development.

· Familiarity with MLOps practices and cloud-based ML deployment.

· Awareness of responsible AI principles (fairness, robustness, safety).

Core Competencies

· Strong analytical and problem-solving skills, with the ability to translate business problems into data solutions.

· Ability to design scalable, reliable, and maintainable data systems.

· Effective communication and stakeholder management skills.

· Self-driven, adaptable, and able to operate in ambiguity.

· Strong sense of ownership and commitment to delivery excellence.

· Passion for leveraging data and technology to deliver public good.

· Minimum of a Bachelor’s Degree in Computer Science, Computer Engineering, Machine Learning / Data Science / AI, or related disciplines;

·        Must have a minimum of 1-2 years of IT experience or Fresh graduates are welcomed

·        Able to understand and apply a range of AI/ML techniques for regression and classification

· Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost,NLTK, spaCy )

· Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g., RAG)

· Experience with context engineering techniques and prompt optimization strategies

· Proficient in git, SQL

· Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy)

Activate Interactive Pte Ltd (“Activate”) is a leading technology consultancy headquartered in Singapore with a presence in Malaysia and Indonesia. Our clients are empowered with quality, cost-effective, and impactful end-to-end application development, like mobile and web applications, and cloud technology that remove technology roadblocks and increase their business efficiency.

We believe in positively impacting the lives of people around us and the environment we live in through the use of technology. Hence, we are committed to providing a conducive environment for all employees to realise their full potential, who in turn have the opportunity to continuously drive innovation.

We are searching for our next team members to join our growing team.

If you love the idea of being part of a growing company with exciting prospects in mobile and web technologies that create positive impact on people’s lives, then we would love to hear from you.

Co-Development Business Unit is hiring for Data Scientist

You will work in Singapore Government Agencies.

This is a fixed term contract. The engagement is 1 year, renewable.

Internal Code: A26126

This is a fixed term contract role. The engagement is 1 year, with option to extend to another 1 year

What will you do?

As a Data Scientist, you will use data science and Generative AI techniques to develop models and products that support a range of divisions across investment promotion, industry development and corporate functions.

You will have the opportunity to partner closely with key stakeholders to identify business challenges, translate requirements into technical solutions, and deliver AI and Machine Learning products that drive measurable outcomes.

• Collaborating with business users to understand their key priorities and use cases; proposing and developing solutions using data science and/or Generative AI techniques to drive business value
• Researching emerging AI/data science techniques and identifying relevant ones for EDB to explore and adopt (e.g. Agentic, LLM, Predictive, Fraud/Anomaly Detection, Text Analytics, Customer Segmentation)
• Data wrangling & analysis - preprocessing, cleaning and feature engineering
• Supporting the daily operations and maintenance of deployed data science models and products, including Assistants on PAIR / AIBots
• Developing backend APIs and services to support AI model deployment and integration
• Building frontend interfaces and user experiences for AI-powered applications
• Documenting changes to existing products
• Reviewing and implementing fixes for reported security vulnerabilities

What are we looking for?

• Minimum of Bachelor’s Degree in Computer Science, Computer Engineering, Machine Learning / Data Science / AI or related disciplines;
• Able to understand and apply a range of AI/ML techniques for regression and classification
• Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost, NLTK, spaCy )
• Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g. RAG)
• Experience with context engineering techniques and prompt optimization strategies
• Proficient in git, SQL
• Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy)
• Proficient in modern programming languages (e.g. typescript, C#)
• Experience in cloud platform and services preferably in AWS
• Experience in Docker/Kubernetes
• Experience with web frameworks and full-stack development, including backend frameworks (e.g. FastAPI, Flask, Express.js) and RESTful API development, and frontend technologies (e.g. React, Vue.js, HTML/CSS, TypeScript)
• Experience with LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face Transformers)
• Knowledge of vector databases and embedding techniques (e.g. Pinecone, Chroma, FAISS)
• Understanding of AI agent frameworks and multi-agent system

What do we offer in return?

• Fun working environment
• Employee Wellness Program
• To work in Singapore Government Agencies projects
• We provide structured development framework and growth opportunities. (We are a “SHRI 2025 Gold winner” in “Learning & Development; Coaching & Mentoring”)

Why you'll love working with us?

If you are looking for opportunities to collaborate with leading industry experts and be surrounded by highly motivated and talented peers, we welcome you to join us. We provide all employees with equal opportunities to grow and develop with us. We believe your success is our success.

Activate Interactive Singapore is an equal opportunity employer. Employment decisions will be based on merit, qualifications and abilities. Activate Interactive Pte Ltd does not discriminate in employment opportunities or practices on the basis of race, colour, religion, sex, sexuality, national origin, age, disability, marital status or any other characteristics protected by law.

Protecting your privacy and the security of your data are longstanding top priorities for Activate Interactive Pte Ltd.

Your personal data will be processed for the purposes of managing Activate Interactive Pte Ltd’s recruitment related activities, which include setting up and conducting interviews and tests for applicants, evaluating and assessing the results, and as is otherwise needed in the recruitment and hiring processes.

Please consult our Privacy Notice () to know more about how we collect, use, and transfer the personal data of our candidates. Here you can find how you can request for access, correction and/or withdrawal of your Personal Data.

COMPANY DESCRIPTION

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together - building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities - all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

RESPONSIBILITIES

• Data Management and Analytics:
• Collect, clean and transform data from multiple internal and external sources, including APIs, space and booking management systems and smart meters.
• Build interactive and user-friendly dashboards, reports and data models to support business decision-making.
• Gather business requirements from different functional teams and translate them into suitable data requirements.
• Ensure data quality, consistency and governance across the organization.
• Conduct thorough data analysis to identify trends, patterns, and performance metrics that influence business decisions.
• Generate actionable insights through advanced analytics performed on diverse data gathered from large longitudinal cohorts, continuous sensors, and combined datasets.
• Develop compelling data-driven narratives for impactful storytelling to both internal and external stakeholders.
• Maintain library of OCIS data, reviewing sources of data, and recommend improvements to data sources and collection.
• Ensure data integrity and establish standardized reporting processes.
• Integrate OCIS data with broader university analytics to inform decision-making.
• Dashboard Optimisation:
• Conduct usability audit on existing dashboards.
• Develop and optimize data models within the dashboarding tool for reporting and analytics.
• Optimize layout for clarity and user experience.
• Develop predictive models to forecast resource needs eg based on faculty expansion, student enrolment, and operational changes.
• Champion Experimentation:
• Design, implement, and analyse A/B tests and multivariate experiments to drive continuous improvement.
• Stay updated with the latest management technologies and automation tools.
• Propose, implement, and optimize technological solutions to enhance data analysis, reporting capabilities, and overall transportation efficiency.
• Stakeholder Engagement & Collaboration:
• Develop clear documentation of metric definitions and logic to ensure alignment across teams. Eg user Manuals and guidelines for stakeholders.
• Engage regularly with cross-functional stakeholders to understand their workflows, pain points, and evolving data needs.
• Translate business questions into actionable metrics and analytical solutions to support procurement and FMS operations.
• Collaborate with cross-functional teams to gather requirements, define data-related technical issues, and provide solutions.
• Provide user training and develop user guide and documentation.
• Automate manual processes and create efficient digital workflows.
• Support Environmental Sustainability in the Workplace:
• Collaborate with other teams on Sustainability related data and functions.
• Ensure alignment and accuracy of OCIS-provided data for organization-wide Sustainability reports.
• Participate in relevant meetings to understand stakeholder needs.
• Develop visuals that support communication and decision-making during Sustainability related meetings and reviews.
• Other duties as assigned.

• Bachelor's degree or higher in Computer Science, Information Systems, Engineering, Business Analytics, Statistics or any related quantitative discipline/field.
• Other disciplines can be supplemented with additional qualifications and prior experience in Data Science / Data Analytics.
• 3 to 5 years of experience in Data Analytics, Business Intelligence, Data Engineering or relevant quantitative experience in the Real Estate / Facilities industry.
• Proficiency in data cleaning, analytics & visualisation tools (Power BI, Tableau, SQL).
• Good understanding of AI-driven scheduling and IoT-based tracking for real-time space monitoring.
• Strong proficiency in Excel and hands-on experience with querying and manipulating large, complex datasets with tools e.g. SQL, Python.
• Strong proficiency in statistical analysis and data mining techniques.
• Strong proficiency in Python (or R) for advanced statistical analysis, data modelling, and automation (e.g., pandas, numpy, scikit-learn).
• Proficiency with data visualization tools (e.g., Tableau, Looker, Power BI) to build effective and clear dashboards.
• Experience using dataflowgen 2/ Microsoft fabric.
• Experience writing PowerBI DAX function.
• Experience designing interactive and user-friendly dashboards with UI principles.
• Familiar with machine learning techniques and predictive modelling.
• Strong analytical mindset and problem-solving abilities.
• Strong stakeholder management (technical and non-technical) and communication skills.
• Willingness to challenge the status-quo and motivate the team to foster the data-driven culture.
• Proven experience in data analysis, reporting, and solution development within the real estate / infrastructure / Utilities industry.
• Familiar with resource and booking management systems.
• Familiar with Al tools, chatbot development, or workflow automation is a plus.
• Good understanding of space planning and facilities management principles and techniques to optimize the use of resources while adhering to regulatory compliance.
• Strong writing and presentation capabilities.
• Experience in space management software (Planon, AutoCAD).
• Knowledge and experience in data governance, data security, and privacy practices.

#LI-JN2

Candidates who do not possess the stipulated qualifications but have relevant work experience may still apply. Remuneration and appointment terms shall commensurate with qualifications and experience. SMU reserves the right to modify the appointment terms where necessary.

• Contract role with leading bank in Singapore with 2 headcounts ending 31st Dec 2026
• This role will sit in Account Control Monitoring team.

Responsibilities:

• Execute remediation plans to cleanse and standardize client data in accordance with the bank’s requirements
• Ensure prioritized data quality issues raised are promptly identified and remediated
• Work with cross-functional teams to identify and implement more efficient solutions for data quality issues, including automation of manual tasks
• Investigate appropriate corrective actions by reviewing source documentation where required
• Perform the corrective actions and where deemed required, recommend process or systems change or preventive controls to avoid recurrence
• Provide weekly reports on the data remediation progress for efficient tracking
• Ensure appropriate and confidential handling/sharing of sensitive client data in compliance with the bank’s policies and procedures
• Identify red flags related to data quality or compliance and escalate in accordance with policy and procedures
• Ensure timely follow-up and/or escalation to relevant stakeholders as required
• Complete delegated and ad-hoc tasks within the established timeframes to support the overall data remediation objectives

Qualifications:

• Bachelor's degree or at minimum diploma holder
• Meticulous and detail oriented
• At least 2 years of experience relating to banking operations, preferably in Client Onboarding & Maintenance functions, client documentation and static data maintenance
• Able to conduct and apply quality testing approaches in day-to-day activities
• Good written and verbal communication skills
• Able to thrive in a team, is highly approachable and open minded
• Able to be proactive in establishing processes and adopting best practices
• Is methodological with an eye for details and possess the ability to work independently
• Proficient in MS Office, particularly Excel, PowerPoint

Next Steps

• Drop your resume and contact us to follow-up, or send your resume to amos.tan@adecco.com
• Only shortlisted candidates will be contacted

Amos Tan Jun Han

EA Licence Number: 91C2918

Personnel Registration Number: R23113385