Quantitative Researcher Equities

DRW · Singapore

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.
Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.
We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.
We are seeking a Quantitative Researcher to work on alpha generation, model development, and the full research pipeline for mid-frequency equity statistical arbitrage strategies. The position will collaborate closely with the Portfolio Manager and other team members, contributing across all stages — from data processing and feature engineering to backtesting and live deployment.
Key Responsibilities

Research, design, and implement predictive signals and features across global equities.
Work with large, diverse datasets; develop robust feature engineering and point in time (PIT) data cleaning pipelines.
Collaborate with team members to implement research infrastructure, backtesting frameworks, and analytical tools.
Build frameworks and tools for performance attribution and ongoing live strategy monitoring.

Requirements

2–8 years of experience in quant equities, with a primary focus on alpha research.
Familiarity with both standard and alternative datasets used in equity stat arb strategies, including data cleansing, ticker mapping, point-in-time (PIT) handling, and other dataset idiosyncrasies.
Strong programming skills in Python; experience with SQL and distributed data environments.
Advanced degree (PhD/MSc) in Mathematics, Physics, Statistics, Computer Science, or related quantitative discipline.

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