About Us
Bright Point Capital Pte Ltd is a registered fund management company in Singapore regulated by the Monetary Authority of Singapore (MAS). We are currently looking to expand our research and investment capabilities for one of our funds.
The investment mandate focuses on trading financial instruments across different asset classes and global exchanges. We are looking for a strong quantitative researcher to support systematic strategy research, model validation, and data-driven decision making.
ROLE DESCRIPTION
- Work closely with experienced traders and developers to research, test, and validate systematic trading models
- Apply mathematics, statistics, machine learning, and scientific research methods to large-scale financial market data
- Support the trading team with rigorous research evidence; trading direction, strategy prioritization, and live deployment decisions will remain led by the trading team
- Prior finance or trading experience is not required
RESPONSIBILITIES
- Translate market observations and trading ideas into testable research hypotheses.
- Research statistical and machine learning methods for forecasting, classification, signal generation, and pattern detection in noisy sequential data.
- Evaluate modern ML approaches, including deep learning, sequence models, and attention-based models, while benchmarking against simpler statistical baselines.
- Build reusable research tools for data cleaning, feature generation, model comparison, experiment tracking, and result analysis.
- Communicate research results clearly, including assumptions, limitations, risks, negative findings, and reasons a model may fail in live use.
REQUIREMENTS
- PhD preferred, or exceptional MSc/BSc, in mathematics, statistics, physics, computer science, electrical engineering, operations research, machine learning, or a related quantitative discipline.
- Strong foundation in probability, statistics, optimization, linear algebra, machine learning, and time-series or stochastic modelling.
- Strong programming ability in Python and experience with scientific computing or machine learning libraries such as NumPy, pandas, scikit-learn, PyTorch, JAX, TensorFlow, or similar tools.
- Experience working with noisy real-world data, large datasets, simulations, or empirical research problems.
- Strong problem-solving ability, intellectual honesty, attention to detail, and willingness to challenge model results rather than accept them at face value.
WHAT TO EXPECT
- You will learn the financial market context from experienced traders while contributing your scientific and technical expertise.
- We encourage rigorous thinking, reproducible research, and honest communication of both positive and negative results.
- The work involves open-ended research problems where clear thinking, persistence, and careful validation matter more than using complex models for their own sake.
