Senior Quantitative Researcher – Risk Modeling

🕒 March 20

🏄 California – Remote

🏄 California – Remote

💵 $165k / year

⏰ Full Time

🟠 Senior

🎲 Risk

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Swish Analytics

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

🎲 Gambling

🤖 Artificial Intelligence

🤝 B2B

💰 $6.9M Series B - Swish Analytics on 2019-05

Gambling • Artificial Intelligence • B2B

Swish Analytics is a machine-learning driven sports-odds and predictive-data company that builds a real-time odds origination engine for sportsbooks and fantasy platforms. It provides hyper-accurate player prop pricing, match and team markets, in-play and pre-game projections, and automated bet-lifecycle management to power bookmaking, bet builders, parlays, and micro-markets. Swish positions itself as a B2B provider focused on automation, accuracy, and scalable odds infrastructure for the sports betting industry.

📋 Description

• 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 • 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