Senior Quantitative Researcher - Risk Modeling

Core Responsibilities

• Own end-to-end research and production pipelines for a strategy
• Lead alpha research initiatives leveraging 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
• Proactively engage with traders and infrastructure teams to clarify research objectives and resolve data dependencies

Risk Modeling

• 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, leverage constraints, and implement automated risk controls

Requirements

• Minimum of 5 years of experience in quantitative research, systematic trading, or statistical modeling
• Master's degree in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Financial Engineering) strongly preferred; PhD a plus
• Expert-level Python skills; able to build production-grade research and trading systems
• Strong SQL skills; 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; 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 Have

• 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 FIX protocol, Betfair or Matchbook API experience, and ultra-low-latency trading infrastructure

About Swish Analytics

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We deliver odds origination, risk management & trading software for the core four U.S. sports.