Quant Researcher

About the Role

We are looking for a Senior Quantitative Researcher to design, test, and deploy systematic trading strategies in US markets. This role suits someone who has taken strategies from idea to production, understands what separates a good backtest from a strategy that survives live, and is comfortable working close to the metal in a low-latency, high-frequency environment. You will own the full research lifecycle, from signal discovery to live deployment and monitoring, working alongside engineers, traders, and risk.

What You Will Do • Research, develop, and validate systematic strategies (market making, statistical arbitrage, short-horizon alpha, or related approaches) across US equities, futures, and/or options. • Build and maintain realistic simulation and backtesting frameworks that account for queue position, latency, fees, slippage, and market impact. • Analyze high-frequency tick and order book data to find predictive signals and improve execution quality. • Work with the infrastructure and engineering teams to take strategies into production, including latency-sensitive components, co-location setups, and exchange connectivity. • Monitor live performance and run post-trade analysis (P&L attribution, adverse selection, fill rates, slippage, drawdown), then iterate based on the data. • Define and enforce risk parameters, position limits, and kill-switch logic in coordination with the risk team. • Document research findings clearly and present results to the team and senior stakeholders. • Mentor junior researchers and raise the team's research standards.

What We're Looking For Experience • 5+ years of experience in quantitative research or trading at a prop firm, hedge fund, market maker, or HFT shop. • A proven record of building and deploying strategies that ran live, not just in backtests, with verifiable performance (Sharpe, drawdown, capacity, hit rate). • Hands-on experience with US markets, including their structure (NMS, ATS/dark pools, exchange fee tiers, order types, auction mechanics).

Technical Skills • Strong grounding in market microstructure, order book dynamics, and execution. • Strong statistical and ML foundations: time-series analysis, signal processing, regularization, and an awareness of overfitting and data-snooping risks.• Proficiency in Python (NumPy, pandas, scikit-learn, etc.) for research, plus working knowledge of C++ or another low-latency language. • Comfort working in an HFT infrastructure environment: tick-level data, FIX/binary exchange protocols, market data feeds (e.g., ITCH, OUCH, PITCH), co-location, and latency measurement and optimization. • Experience handling large datasets (tick data, L2/L3 order books) with tools such as kdb+/q, Parquet, or similar. Mindset • Ownership and the ability to work independently with minimal oversight. • Intellectual honesty about what works, what doesn't, and why. • Clear communication of complex ideas to both technical and non-technical audiences. Nice to Have • Experience with options market making or volatility strategies. • Familiarity with FPGA or kernel-bypass technologies. • Advanced degree (MS/PhD) in Mathematics, Statistics, Physics, Computer Science, Engineering, or a related quantitative field. • Experience across multiple asset classes or global venues.

Why Join Us • Real ownership of research and strategies, with a direct line from your work to live P&L. • A performance-driven culture with [compensation structure: base + performance bonus / profit share]. • Access to high-quality data, modern infrastructure, and low-latency connectivity. • [Flexible / remote working arrangements.] • A clear growth path to lead research or run your own book.

How to Apply Please share your CV along with a brief overview of the strategies you have built and deployed, the asset classes and venues they ran on, and (where you're able to disclose) key live performance metrics and drawdown history. Compensation and terms will be discussed directly with shortlisted candidates.