qSpark

Role Description 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. - 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. Qualifications - 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.