Senior Quantitative Researcher – Risk Modeling

🕒 March 20

🏄 California – Remote

🏄 California – Remote

💵 $165k / year

⏰ Full Time

🟠 Senior

🎲 Risk

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 26%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Swish Analytics

WebsiteLinkedInAll Job Openings

11 - 50 employees

🎲 Gambling

🎮 Gaming

⚽ Sports

💰 $6.9M Series B on 2019-05

Gambling • Gaming • Sports

Swish Analytics is a company that specializes in machine learning for US sports betting and fantasy sports. They deliver accurate, algorithm-driven predictions and tools to help improve betting strategies across major sports leagues such as the NBA, NFL, MLB, and NHL. Swish Analytics offers tools like lineup optimizers for daily fantasy sports and is an Authorized MLB Data Distributor to U. S. sports betting operators. Their team consists of data scientists, machine-learning engineers, and developers dedicated to providing powerful analytics and innovative solutions for the sports betting and gaming 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

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