Senior Quantitative Researcher - Risk Modeling San Francisco, CA - Remote (San Francisco)
Senior Quantitative Researcher - Risk Modeling
Company Description Swish Analytics is a sports analytics and trading company building the next generation of predictive sports analytics and exchange-based trading products. We believe that profitable trading is a challenge rooted in engineering, mathematics, and market expertisenot intuition. We're seeking team-oriented individuals with an authentic passion for quantitative trading who can execute in a fast-paced environment without sacrificing technical excellence.
As we expand our presence on betting exchanges, we're building infrastructure and strategies akin to those found in traditional financial markets. Our challenges are unique, and we hope you're comfortable in uncharted territory.
Role Overview
As a Senior Quantitative Researcher, you will own end-to-end research and production pipelines for one or more trading strategies. You'll lead research initiatives that generate alpha and improve execution quality, mentor junior researchers, and collaborate closely with our Trading desk to translate quantitative insights into profitable systematic strategies while maintaining rigorous risk management.
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 submillisecond 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 12 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 realtime risk monitoring systems across multiasset portfolios
• Build models for dynamic position sizing, portfolio optimization, and factor exposure management
• Develop stress testing and scenario analysis frameworks for tailrisk events and regime changes
• Collaborate with Trading and Risk Management to define VaR limits, leverage constraints, and implement automated risk controls
Requirements
• 58 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
• Expertlevel Python skills; able to build productiongrade research and trading systems
• Strong SQL skills; experience with complex queries on tick databases and timeseries 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 highfrequency tick data and realtime market feeds
• Familiarity with AWS or similar cloud infrastructure for largescale 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 enterprisegrade risk management systems with realtime 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 performancecritical 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 ultralowlatency trading infrastructure
Equal Opportunity Employer
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employers discretion, this position may require successful completion of background and reference checks.
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Apply Now
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Company Description Swish Analytics is a sports analytics and trading company building the next generation of predictive sports analytics and exchange-based trading products. We believe that profitable trading is a challenge rooted in engineering, mathematics, and market expertisenot intuition. We're seeking team-oriented individuals with an authentic passion for quantitative trading who can execute in a fast-paced environment without sacrificing technical excellence.
As we expand our presence on betting exchanges, we're building infrastructure and strategies akin to those found in traditional financial markets. Our challenges are unique, and we hope you're comfortable in uncharted territory.
Role Overview
As a Senior Quantitative Researcher, you will own end-to-end research and production pipelines for one or more trading strategies. You'll lead research initiatives that generate alpha and improve execution quality, mentor junior researchers, and collaborate closely with our Trading desk to translate quantitative insights into profitable systematic strategies while maintaining rigorous risk management.
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 submillisecond 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 12 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 realtime risk monitoring systems across multiasset portfolios
• Build models for dynamic position sizing, portfolio optimization, and factor exposure management
• Develop stress testing and scenario analysis frameworks for tailrisk events and regime changes
• Collaborate with Trading and Risk Management to define VaR limits, leverage constraints, and implement automated risk controls
Requirements
• 58 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
• Expertlevel Python skills; able to build productiongrade research and trading systems
• Strong SQL skills; experience with complex queries on tick databases and timeseries 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 highfrequency tick data and realtime market feeds
• Familiarity with AWS or similar cloud infrastructure for largescale 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 enterprisegrade risk management systems with realtime 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 performancecritical 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 ultralowlatency trading infrastructure
Equal Opportunity Employer
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employers discretion, this position may require successful completion of background and reference checks.
#J-18808-Ljbffr
Apply Now
Apply Now