Quantitative Developer (Data scientist) - Large Asset Owner

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.2. CORE RESPONSIBILITIESA. 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 combinationPortfolio optimization and risk models (Mean-Variance, Black-Litterman, Risk Parity)Performance attribution and risk decomposition modelsTranslate 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.3. TECH STACKMust Have: Python (Pandas, NumPy, Scikit-learn, Statsmodels), SQL, PySpark / Dask, Git, LinuxModel Stack: MLflow, Airflow / Prefect, ML Libraries (XGBoost, LightGBM, PyTorch/TensorFlow)Strong Plus: KDB+/q, C++ for performance, Snowflake / Databricks, Power BI / Dash for visualizationInvestment Stack: Barra / Axioma risk models, Aladdin / BlackRock, FactSet, Bloomberg4. IDEAL PROFILEEducation: 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.5. 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.Only Shortlisted candidates will be notified.