Quantitative Researcher

About the role

A newly created Quantitative Researcher position in an investment office, recruited through a consultant, to build and grow the portfolio of a family investment vehicle and active private equity investor. The role sits within Banking & Financial Services, quantitative analysis, and is based in Singapore.

Key responsibilities

• Build, test and validate quantitative investment models using mathematical, statistical and computational methods.
• Support systematic equity strategies, including factor-based models, statistical arbitrage and relative value.
• Assist in derivatives research such as options pricing, volatility modelling and hedging.
• Formulate and empirically test research hypotheses, separating robust insights from noise and data-mining bias.
• Acquire, clean, process and analyse financial, market and alternative data, then write reproducible, documented research code.
• Run back-tests with realistic transaction costs, market impact and trading constraints, and check out-of-sample performance and overfitting.
• Prepare portfolio and risk analytics on performance, factor exposures, concentration, liquidity and market risk.
• Support quantitative due diligence on external fund managers and products, and present findings and model limitations to the investment team.

Required profile

• Master's degree in a quantitative discipline such as mathematics, statistics, physics, computer science, engineering, operations research, finance or a related field.
• Experience in quantitative research, systematic investing, trading, asset management, derivatives or fintech; strong recent graduates with research or internship experience will be considered.
• Economics, finance, financial engineering or econometrics background is desirable but not essential for candidates with exceptional mathematical and computational ability.
• Clear written and verbal communication, able to explain technical results simply.

Required skills

• Strong foundation in mathematics, probability, statistics, linear algebra and optimisation.
• Programming, preferably Python (C/C++ an advantage), with data analysis and statistical or scientific libraries.
• Statistical inference, regression and time-series analysis; econometrics or machine learning is an advantage.
• Understanding of out-of-sample testing, look-ahead bias, survivorship bias and overfitting.
• Familiarity with version control, reproducible research and large datasets.

What we offer

• Competitive remuneration package with good culture and benefits.
• Exposure to global markets and multi-asset classes.