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Data Scientist - Artificial Intelligence/Machine Learning

Deqode

3.5
3 reviews
Deqode
Job Type   /   Job Level
Full-time   /   Others/Any
Job Location
Greater Kolkata Area
Salary Range
INR 120,000 - INR 180,000 (Monthly)
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Job Description

We are seeking an experienced AI ML Engineer/AI Data Scientist to join our team, with a strong background in Artificial Intelligence and Machine Learning. The ideal candidate will have 4 -7 years of experience in AI/ML and a proven track record of designing and deploying deep learning concepts to generate business value.

Key Responsibilities

  • Designing deep learning concepts and deploying machine learning solutions to generate business value
  • Applying statistical and calculus concepts to drive business outcomes
  • Working in the domains of Natural Language Processing (NLP) and/or Computer Vision (CV)
  • Utilizing machine learning techniques like Random Forest, Support Vector Machine, and Gradient Boosting Machine
  • Applying deep learning techniques across Recurrent Neural Networks, Convolutional Neural Networks, and Transformer Architectures
  • Developing and deploying Generative AI techniques like RAG, Agentic Workflows, and Graph RAG

Required Skills

  • Experience : Experienced AI Scientist with 4 - 6 years of experience in AIML.
  • Design : Designing deep learning concepts.
  • Mathematics : Good in Statistical and Calculus concepts.
  • Domains : Applied in the domains of Natural Language Processing (NLP) and / or Computer Vision (CV).
  • Deployment : Generative AI and deploying Machine Learning (ML) solutions to generate business value.
  • Statistical Methods : Solid theoretical and practical knowledge of some or most econometric/statistical methods like Linear Regression, Logistic regression, Generalized Linear Model, Survival Analysis, Sampling

Techniques, Time Series Analysis, CART, CHAID, Clustering, Discriminant Analysis, Principal Component Analysis, Factor Analysis, Multidimensional Scaling etc.

  • Machine Learning : Machine Learning techniques like Random Forest, Support Vector Machine, Gradient Boosting Machine, XGBoost etc.
  • Deep Learning : Deep Learning techniques across Recurrent Neural Networks, Convolutional Neural Networks etc. and Transformer Architectures as applied in the domains of Natural Language Processing (NLP) and / or Computer Vision (CV) with a consistent track record of building and successfully deploying Machine Learning solutions with demonstrable value to the organization.
  • Generative AI : Generative AI techniques like RAG, Agentic Workflows, Graph RAG, Model Tuning etc.

(ref:hirist.tech)
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