Quantitative Researcher

Job not on LinkedIn

🔥 2 minutes ago

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

qSpark

WebsiteLinkedInAll Job Openings

11 - 50 employees

💸 Finance

💳 Fintech

🏢 Enterprise

Finance • Fintech • Enterprise

qSpark is a leading provider of ultra low latency trading platforms for high-frequency algorithmic trading. The best algorithms are a perfect synthesis of quantitative rigor and insight. With qSpark’s powerful and flexible platforms, traders can realize the full potential of their algorithms in the market. Our technologists work hard and are constantly challenged to meet their personal best, creating a dynamic and entrepreneurial environment in which the best minds collaborate to push the technological envelope.

📋 Description

• 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

• 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