Point72 - Quantitative Strategist, Macro Technology
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Requirements
• Post-graduate degree in a quantitative discipline from a top-tier university. • 12-15+ years of experience in macro quantitative analytics and development at a top-tier bank or fund. • Strong expertise in linear Rates and FX products, including curve construction, pricing, and relative value. • Proven experience building pre-trade analytics, trading pricing tools and research platforms. • Strong understanding of backtesting frameworks, historical analysis, and scenario-based research. • Advanced programming skills in Python and C++. • Solid data engineering and analytics proficiency, including experience with large financial datasets and time-series data. • Proven experience building and persisting derived data at scale, including data modeling, storage, and access patterns for quantitative research and production analytics. • Hands-on experience with cloud-based data and analytics platforms such as AWS, GCP, and/or Azure. • Experience designing and maintaining production-quality data pipelines and analytics services. • Strong communication skills and a demonstrated ability to work closely with investment professionals. • Commitment to the highest ethical standards.
Responsibilities
• Design and develop pre-trade pricing and analytics tools for investment professionals, supporting Rates and FX products. • Implement real-time pricing engines and historical pricing frameworks using market and trading data. • Develop analytics for historical curve construction, curve evolution, and relative value analysis. • Design, generate, and maintain large-scale derived macro datasets for trading analytics, ensuring consistency, performance, and reusability across workflows. • Build scenario analysis frameworks allowing investment professionals to define shocks and assess pricing and PnL impacts. • Integrate pricing, scenario, and backtesting analytics into research and trading workflows. • Develop scalable data pipelines for historical market data ingestion, normalization, storage, and retrieval. • Collaborate with investment professionals to translate trading ideas into quantitative analytics and tooling. • Contribute to the firm’s core analytics libraries and data architecture, with a focus on robustness, performance, and extensibility across asset classes.
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