Senior Quantitative Researcher - Risk Modeling

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.

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

5–8 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

Base salary :

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.