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 programmes that develop them on both professional and personal levels. In addition to attractive remuneration packages, we offer non‑financial benefits and opportunities to develop your potential within OCBC Group’s global network of subsidiaries and offices. Responsibilities Work as a highly skilled and motivated Quantitative Researcher in our investment research team, applying data science, machine learning and advanced portfolio optimisation techniques to improve investment decision‑making, risk management and systematic investment strategies. Support the Senior Portfolio Strategist in driving the multi‑asset analytical framework that underpins Bank of Singapore’s strategic asset allocation (SAA) and tactical asset allocation (TAA) processes. Implement, maintain and enhance a robust quantitative asset allocation model that leverages global economic forecasts, cross‑asset dynamics, risk premia and other investment variables. This model will guide research, discretionary portfolio management, advisory portfolio management and portfolio advisory processes for wealth management clients. Collaborate 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 optimisation, 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 optimisation frameworks to balance return, risk, transaction costs and liquidity constraints. Apply advanced methods such as robust optimisation, Bayesian modelling, Black‑Litterman, factor models and deep learning techniques. Ensure models are scalable, robust and interpretable. Design a framework to identify key economic and market variables, financial conditions and policy settings that impact model 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, advisory portfolio management, technology, and risk). Ensure the proprietary asset allocation model is risk‑based, implementable into BOS client portfolios via DPM and APM strategies and 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 or applied research. Strong quantitative and analytical skills with a solid academic foundation in probability, statistics, optimisation and machine learning. Proficiency in Python and its scientific libraries (NumPy, pandas, scikit‑learn, PyTorch, TensorFlow). Experience with portfolio theory, optimisation algorithms and risk management techniques. Strong background in machine learning and time‑series modelling (forecasting, anomaly detection, regime identification). 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. Excellent written and communication skills; ability to present complex technical concepts clearly. Advanced degree in Finance or Economics and familiarity with financial software such as Bloomberg; knowledge of BlackRock Aladdin is a plus. Seniority Level Mid‑Senior level Employment Type Full‑time Job Function Finance and Sales #J-18808-Ljbffr