Senior Quantitative Researcher - Market Microstructure

Swish Analytics•San Francisco, CA

6d•$155,000

About The Position

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're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients. 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. 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.

Requirements

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
Deep understanding of equity/futures market microstructure, order types, and matching engine logic
Experience building sophisticated TCA frameworks with market impact decomposition
Familiarity with FPGA or kernel-bypass networking for latency-critical applications
5–8 years of experience in quantitative research, systematic trading, or statistical modeling

Nice To Haves

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

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
Design and implement transaction cost analysis (TCA) frameworks to measure and improve execution quality
Build market impact models and optimal execution strategies to minimize slippage across venue types
Develop real-time monitoring systems for latency, adverse selection, and execution performance metrics
Partner with infrastructure teams on ultra-low-latency data feeds and smart order routing algorithms