Description
Design, implement, and validate quantitative models for pricing, risk, portfolio optimization, and relative value across asset classes. Calibrate and backtest models, build robust statistical and econometric frameworks, and partner with trading, risk, and engineering to deliver production-quality analytics.
- • Translate business needs into precise model and data requirements.
- • Provide valuation and data analytics support to traders and researchers.
- • Track model performance, drift, and production health metrics.
- • Design, implement, and unit-test models and supporting analytics.
- • Research new products, datasets, and modeling techniques for applicability.
- • Maintain, refactor, and extend pricing and risk model libraries.
- • Write clear methodology, validation, and results documentation.
- • Interpret model outputs and explain drivers and limitations to stakeholders.
- • Build reusable quantitative components using advanced statistical and econometric methods.
- • Specify model assumptions, calibration targets, and data collection protocols.
- • Gather requirements from trading and risk teams for new or improved models.
- • Partner with quants and analysts to align models with strategy and market dynamics.
- • Collaborate with engineering to productionize, scale, and monitor models.
- • Develop tools for portfolio construction, optimization, attribution, PnL explain, and pricing.
- • Create challenger models and benchmarking frameworks to validate results.
- • Apply statistical, econometric, and ML techniques to valuation, trading, and risk.
- • Calibrate and backtest models; perform sensitivity, scenario, and stress tests.
- • Ensure compliance with model risk management policies and audit standards.
- • Optimize numerical methods, runtime performance, and cloud execution.
- • Incorporate ESG or climate risk factors into models when relevant.
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Financial Services
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O*NET occupational data (work activities, skills, knowledge).
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Last reviewed: Jan 2026
