Quantitative Researcher

Quant Researcher, HFT Futures

Firm: Leading global proprietary trading firm

About the firm

Our client is a highly successful proprietary trading firm. It trades only its own capital, with no clients and no outside investors. It’s a leader in high-frequency and low-latency trading across global futures markets. The culture is collaborative and driven by technology, with small, flat teams where researchers own their ideas from first hypothesis through to live trading and PnL.

The role

You’ll join a small, high-impact team researching and building fully automated high-frequency strategies on major futures exchanges, including CME, Eurex, ICE and SGX. The role is hands-on and end to end: you’ll find alpha in tick-level and order book data, build signals and execution logic, and work closely with traders and engineers to take strategies live and improve them.

What you’ll do

• Research and develop short-horizon alpha signals from tick-level market data and full-depth order book data.
• Build, backtest and validate HFT strategies with rigorous statistics, accounting for latency, queue position, fees and market impact.
• Model market microstructure: order flow, liquidity, price formation and cross-asset or cross-venue relationships.
• Optimise execution, fill probability and risk management for live strategies.
• Monitor live performance, diagnose PnL drivers and keep improving strategies as market conditions change.
• Work with software engineers on research infrastructure, simulators and low-latency production code.

What we’re looking for

• 2+ years in quantitative research at an HFT or market-making firm, ideally in futures. A track record of strategies that made live PnL is strongly preferred.
• An advanced degree (PhD or MSc) in a quantitative field such as mathematics, statistics, physics, computer science or engineering, from a top university.
• Strong grounding in statistics, time-series analysis and machine learning, applied to noisy, high-frequency data.
• Excellent programming skills in Python, plus C++ for performance-critical research or production code.
• A deep understanding of futures market microstructure, matching engines and order book dynamics.
• Comfort working with large datasets (billions of rows) and a strong engineering mindset around research tooling.
• An entrepreneurial, PnL-focused approach. You’re intellectually honest about what works and what doesn’t.

Nice to have

• Experience across several futures asset classes (rates, equity indices, commodities, FX).
• Experience with kdb+/q or similar time-series databases.
• Knowledge of FPGA or ultra-low-latency trading systems.

What’s on offer

• Highly competitive compensation: market-leading base plus a significant discretionary or PnL-linked bonus.
• Direct ownership of strategies, with clear visibility of your impact on PnL.
• Best-in-class infrastructure, data and computing resources.
• A collaborative, non-hierarchical environment alongside some of the strongest researchers in the industry.
• [Benefits: relocation support, visa sponsorship, health cover, meals, etc.]