Job Description
Our client is building a next-generation Quantitative Investment Platform to drive data-driven decision making across its multi-billion dollar global public markets portfolio.
We are seeking a Quant Developer with a strong Data Science and Model Development background - someone who can sit at the intersection of Data Engineering, Quantitative Research, and Portfolio Technology.
You will be responsible for building robust data ingestion \& cleansing pipelines, developing predictive models and factor libraries, and enabling accurate data integration for quantitative research and portfolio construction.
- CORE RESPONSIBILITIES
A. Data Science \& Data Engineering (40%)
- Design, build and maintain end-to-end data ingestion, cleansing, validation and feature engineering pipelines for structured and unstructured data (market, fundamental, macro, ESG, alternative data).
- Develop data quality framework - anomaly detection, point-in-time integrity, survivorship bias controls, corporate actions adjustment.
- Build centralized Quant Data Lake / Feature Store for research consumption.
- Integrate vendor data: Bloomberg, FactSet, MSCI Barra, Axioma, RavenPack, etc.
B. Quantitative Model Development (40%)
- Partner with Quantitative Researchers and PMs to develop, implement, and test quantitative models:
- Cross-sectional equity factor models (Value, Quality, Momentum, Growth)
- Alpha signal research and combination
- Portfolio optimization and risk models (Mean-Variance, Black-Litterman, Risk Parity)
- Performance attribution and risk decomposition models
- Translate prototype models (Jupyter/Python) into production-grade, scalable, backtestable code.
- Implement statistical and ML models: Regression, Time-Series Forecasting, Clustering, NLP for fundamental data, Supervised Learning for alpha signals.
C. Research Platform \& Productionization (20%)
- Develop research toolkit - backtesting engine, simulation framework, model monitoring and model risk metrics.
- Ensure model accuracy, stability and reliability in both research and production environments.
- Implement MLOps best practices: versioning, experiment tracking (MLflow), model governance and documentation as per Model Risk Management policy of a large asset owner.
- Provide technical specifications for model implementation and maintain model inventory.
- TECH STACK
Must Have: Python (Pandas, NumPy, Scikit-learn, Statsmodels), SQL, PySpark / Dask, Git, Linux
Model Stack: MLflow, Airflow / Prefect, ML Libraries (XGBoost, LightGBM, PyTorch/TensorFlow)
Strong Plus: KDB+/q, C++ for performance, Snowflake / Databricks, Power BI / Dash for visualization
Investment Stack: Barra / Axioma risk models, Aladdin / BlackRock, FactSet, Bloomberg
- IDEAL PROFILE
- Education: Masters / PhD in Data Science, Machine Learning, Statistics, Mathematics, Computer Science, Financial Engineering from top-tier institution.
Bachelors with exceptional experience will be considered.
- Experience: 3-15 years in Quantitative Development / Data Science / Model Development within Buy-Side (Asset Manager, SWF, Pension Fund, Insurance AMC) or Sell-Side QIS / Strats.
Core Experience We Need:
- Built data pipelines for quant research - you understand point-in-time, lookahead bias, and data quality.
- Hands-on model development - not just running models, but developing, validating, and productionizing them.
- Buy-side experience - understanding of long-term, long-only portfolio construction vs short-term trading signals.
NOT a fit: Purely IT / App Dev with no quant modeling exposure, or pure HFT / low-latency developers with no fundamental factor model experience.
- WHY THIS ROLE FOR A CANDIDATE?
- Work on the core investment engine of one of the world's largest pools of capital - impact is at sovereign scale.
- Move away from short-term P\&L pressure of hedge funds to long-horizon, research-driven investing.
- Stable, collegiate, highly confidential environment with deep focus on engineering quality and model governance - ideal for someone from GIC, Temasek, BlackRock, Fidelity, Capital Group, CPP, OTPP background.
- Tax-free package + long-term career growth within the fund.