Quantitative Specialist, Portfolio Strategy

Bank of Singapore opens doors to new opportunities.

At Bank of Singapore, we are constantly on the lookout for exceptional individuals to join our team. We promote a culture of openness, teamwork and fairness. Most importantly, we invest in our people through our programmes that develop them on both professional and personal levels. Besides attractive remuneration packages, we offer non-financial benefits and opportunities to develop your potential within OCBC Group’s global network of subsidiaries and offices. If you have passion, drive and the will to succeed, rise to the challenge today!

This is the broad job description of the job profile. Definitive job description should be reviewed and discussed between you and your manager.

• We are seeking a highly skilled and motivated Quantitative Researcher to join our investment research team. This role is focused on applying data science, machine learning, and advanced portfolio optimization techniques to improve investment decision-making, enhance risk management, and support the development of systematic investment strategies. The successful candidate will play a key role in bridging academic research with practical applications, working closely with portfolio managers, portfolio strategists, and the research team.

• The Quantitative Researcher will support the Senior Portfolio Strategist to drive the multi-asset analytical framework to strengthen Bank of Singapore’s strategic asset allocation (SAA) and tactical asset allocation (TAA) investment process.

• Under the supervision of the Senior Portfolio Strategist, the Quantitative Researcher will implement, maintain and enhance a robust quantitative asset allocation model that leverages global economic forecasts, cross-asset dynamics, risk premia and other investment-related variables. This model will guide research, discretionary portfolio management, advisory portfolio management and portfolio advisory process for wealth management clients.

• The Quantitative Researcher will also work closely with the group and the data team to design and build a Personalized Asset Allocation Engine for the Bank of the Future initiative.

• Quantitative Research & Strategy Development

  • Conduct research in portfolio optimization, risk modelling, and asset allocation.

  • Design and test predictive models for returns, volatility, and cross-asset correlations.

  • Investigate and incorporate alternative and unstructured data sources for investment insights.

  • Maintain and enhance a systematic Bank of Singapore asset allocation model with robust quantitative inputs that is scalable and sustainable for wealth management clients.

• Modelling & Optimization

  • Develop and enhance optimization frameworks to balance return, risk, transaction costs, and liquidity constraints.

  • Apply advanced methods such as robust optimization, Bayesian modelling, Black-Litterman, factor models, and deep learning techniques.

  • Ensure models are scalable, robust, and interpretable.

  • Design framework to identify key economic and market variables, financial conditions and policy settings that would impact the model’s risk and return characteristics.

• Implementation & Collaboration

  • Partner with the Data team to prototype, back-test, and implement research outputs into production systems.

  • Establish reproducible research pipelines with strong validation and monitoring frameworks.

  • Collaborate with cross-functional teams (discretionary portfolio management (DPM), advisory portfolio management (APM), technology, and risk).

  • The proprietary asset allocation model to be developed by the portfolio strategist will be a) risk-based b) implementable into BOS client portfolios via DPM and APM strategies and c) scalable across BOS portfolios.

• Knowledge Sharing & Documentation

  • Maintain comprehensive documentation of methodologies, datasets, and model assumptions.

  • Present research findings and recommendations to internal stakeholders.

  • Contribute to a culture of continuous learning and innovation.

Qualifications

• MSc or PhD in Quantitative Finance, Economics, Computer Science, Statistics, Applied Mathematics, Engineering, or a related field with at least 10 years of experience in data science/ advanced analytics/ applied research

• Strong quantitative and analytical skills and strong academic foundation in probability, statistics, optimization, and machine learning.

• Proficiency in Python and its scientific libraries (NumPy, pandas, scikit-learn, PyTorch, TensorFlow).

• Strong background in machine learning and time-series modeling (forecasting, anomaly detection, regime identification).

• Experience with portfolio theory, optimization algorithms, and risk management techniques.

• Familiarity with databases (SQL/NoSQL), big data tools (Spark, Dask), and cloud platforms (AWS, GCP, Azure).

• Competency in version control (Git) and collaborative coding practices.

• Experience working with large-scale, high-dimensional datasets.

• Strong understanding of macroeconomics, multiple asset classes and the interdependencies of economic and risk factors on investments.

• Intellectual rigour and curiosity, excellent written and communication skills.

• Ability to work in a high-impact environment, requiring strong interpersonal and presentation skills.

• Advanced degree in Finance or Economics and familiarity with financial software such as Bloomberg. Knowledge of BlackRock Aladdin will be a plus.