Senior Quantitative Researcher - Market Microstructure, Vianen NB

Company Description 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.

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

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