Senior Quantitative Researcher - Risk Modeling

Swish Analytics•San Francisco, CA

19h•$150,000 - $250,000

About The Position

Swish Analytics is a sports analytics and trading company developing predictive sports analytics and exchange-based trading products. As a Senior Quantitative Researcher, you will own end-to-end research and production pipelines for one or more trading strategies. You will lead initiatives to generate alpha and improve execution quality, mentor junior researchers, and work closely with the Trading desk to turn quantitative insights into systematic strategies while maintaining rigorous risk management. The role includes research and production work involving high-frequency market data, market microstructure, quantitative strategy development, risk modeling, and real-time monitoring across multi-asset portfolios. It also involves collaborating with Trading, Risk Management, and infrastructure teams. Responsibilities may change at the employer’s discretion, and the position may require successful completion of background and reference checks.

Requirements

At least 5 years of experience in quantitative research, systematic trading, or statistical modeling.
Expert-level Python skills and ability to build production-grade research and trading systems.
Strong SQL skills, including experience with complex queries on tick databases and time-series datasets.
Deep experience with Monte Carlo methods, stochastic calculus, and probabilistic modeling.
Proven ability to develop, backtest, and deploy systematic trading strategies with demonstrable P&L.
Experience processing high-frequency tick data and real-time market feeds.
Familiarity with AWS or similar cloud infrastructure for large-scale backtesting and research.
Track record of mentoring junior quantitative researchers.
Excellent communication skills and ability to present complex quantitative research to portfolio managers and trading desks.
Experience designing enterprise-grade risk management systems with real-time Greeks calculation.
Strong understanding of factor models, correlation structure, concentration risk, and portfolio attribution.

Nice To Haves

Master’s degree in a quantitative discipline such as Mathematics, Statistics, Physics, Computer Science, or Financial Engineering; a PhD is a plus.
Proficiency in Rust, C++, or other systems languages for performance-critical components.
Experience with MLOps, model monitoring, and adaptive retraining pipelines for regime detection.
Background in derivatives pricing, options market making, or volatility arbitrage.
Familiarity with the FIX protocol, Betfair or Matchbook APIs, and ultra-low-latency trading infrastructure.

Responsibilities

Own end-to-end research and production pipelines for one or more trading strategies.
Lead alpha research initiatives using advanced statistical and machine learning techniques.
Process and analyze high-frequency tick data, order book snapshots, and market microstructure signals with sub-millisecond latency requirements.
Analyze price formation, market liquidity dynamics, and limit order book imbalances across electronic venues.
Build and run Monte Carlo simulations to estimate P&L distributions, risk exposures, and portfolio dynamics.
Develop, backtest, and optimize quantitative trading strategies with rigorous statistical validation.
Interpret complex model outputs and communicate alpha-generation mechanisms to portfolio managers.
Write modular, clean, and efficient Python code; build custom analytics libraries and research frameworks.
Lead design reviews and establish data quality and research reproducibility standards.
Guide 1–2 junior researchers through project delivery and model development.
Engage with traders and infrastructure teams to clarify research objectives and resolve data dependencies.
Design and maintain real-time risk monitoring systems across multi-asset portfolios.
Build models for dynamic position sizing, portfolio optimization, and factor exposure management.
Develop stress-testing and scenario-analysis frameworks for tail-risk events and regime changes.
Collaborate with Trading and Risk Management to define VaR limits and leverage constraints and implement automated risk controls.