Job description

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 expertise—not 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 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 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 employer’s discretion, this position may require successful completion of background and reference checks. Base salary is one hundred and fifty to two hundred and fifty thousand (plus bonus), depending on experience.

Requirements

Must-haves

Company 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
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
Minimum of 5 years of experience in quantitative research, systematic trading, or statistical modeling
Expert-level Python skills; able to build production-grade research and trading systems

Nice-to-haves

Master's degree in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Financial Engineering) strongly preferred; PhD 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 FIX protocol, Betfair or Matchbook API experience, and ultra-low-latency trading infrastructure

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonsqlawsrustapi

Platform: teamtailor
Effort: medium