qSpark is looking for a quantitative researcher with a genuine edge in signal research and market microstructure to join our team. This role demands someone who ships research into live strategies and is laser-focused on strategies that achieve high Sharpe in production.

Responsibilities

Research, develop, and continuously improve quantitative trading signals across HFT and near-HFT timeframes, with a relentless focus on high Sharpe, deployable strategies Design and run rigorous backtests that honestly account for microstructure: spread costs, venue behavior, print filtering, adverse selection, and realistic fill modeling — with the explicit goal of performance that holds up in production, not just on paper Collaborate closely with portfolio managers and developers to bring validated research into production and improve live strategies Analyze live performance regularly — decompose P&L, identify regime shifts, attribute slippage — and feed findings back into active research Take on increasing levels of research leadership over time, including mentoring and setting the direction for research cycles

Requirements

Requirements 2+ years of hands-on quantitative research or strategy development in an electronic trading environment, with demonstrated ability to produce high Sharpe strategies that survive live deployment Strong applied statistics: Performance estimation, overfitting stress-tests, non-stationarity, and test design under real market constraints Fluent in Python for research; C++ familiarity a genuine advantage Solid knowledge of market microstructure: TAQ data interpretation, venue differences, short-term price dynamics Track record of taking research across the line into live production — not just exploring Bachelor's or Master's degree in Mathematics, Statistics, Physics, Computer Science, Computational Finance, or a related quantitative field