DV Trading - Quantitative Researcher
Requirements
• 3+ years of experience in a quantitative research or systematic trading role • Strong Python skills across the research stack — data engineering, statistical analysis, backtesting, and visualization (pandas, numpy, scipy, and similar) • Demonstrated experience building research infrastructure or frameworks from the ground up • Deep understanding of market microstructure — order book dynamics, execution quality, and intraday price formation • Experience researching relative value strategies across equities, futures, or other liquid instruments • Rigorous statistical mindset with strong emphasis on out-of-sample validation and avoiding overfitting • Familiarity with APAC market structure and exchange mechanics in one or more target markets • Preferred • Experience with high-frequency or tick-level data pipelines • Background in signal research across multiple asset classes or geographies • Exposure to a prop trading or quantitative hedge fund environment • Familiarity with SQL, cloud data infrastructure, or distributed computing frameworks
Responsibilities
• Own the end-to-end research pipeline: data ingestion, cleaning, storage, and versioning through to signal research, backtesting, and production deployment • Design and build scalable research infrastructure and frameworks in Python, in partnership with the development team • Develop and refine signals grounded in market microstructure analysis — order flow, liquidity dynamics, tick data patterns, and execution analytics • Research and model relative value opportunities across equities, futures, and derivatives within APAC and global markets • Conduct rigorous statistical analysis to evaluate signal quality, decay, and capacity constraints • Collaborate with traders to translate research into deployable strategies with well-defined risk parameters • Drive continuous improvement of existing strategies through systematic performance analysis, signal refinement, and execution optimization • Continuously improve tooling, research workflows, and data coverage to accelerate the research cycle
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