Quantitative Trader

Quant and Tech recruiter – Engineering | Data | AI | Quants | Research

Summary

A quantitative trader with several years of experience in a small, high-ownership pod structure, covering the full lifecycle of research, modelling, implementation, and daily trading decision-making. Strong record of generating new alpha ideas, improving model efficiency, and contributing to systematic trading strategies across global equities and related products.

Core Strengths

• End-to-end exposure across research, quant development, and portfolio/trading decisions.
• Comfortable with tight execution loops and taking ownership of full model pipelines.
• Experienced in debugging production strategies and improving robustness.
• Regular contributor of new features, signals, and ML-driven model improvements.
• Skilled at evaluating new data sources and optimising existing input pipelines.
• Experience with feature engineering, cross-validation techniques, and model diagnostics.
• Background in systematic long/short equities across US and Europe.
• Additional exposure to fixed income and market‑making style risk management.

Technical Skills

• Strong programming background (Python, C++/C#, or similar).
• Experience with production‑grade ML workflows.
• Familiar with distributed compute, model optimisation, and low‑latency considerations.

Small‑Team Versatility

• Works in a 4‑person pod—responsible for everything from research to deployment.
• Able to operate independently with minimal structure.
• Thrives in environments where decisions are fast, data‑driven, and collaborative.

Trading & Research Focus

• Strategies: Systematic L/S equities, with some exposure to fixed‑income signals and hedging activities.
• Style: Medium‑to‑high‑frequency stat‑arb ideas (non‑HFT).
• Daily Activities:
• Monitoring model outputs
• Intraday adjustments to risk
• Evaluating PnL driversRunning daily research iterations
• Implementing improvements to execution logic

Practical Achievements

• Delivered multiple incremental improvements to alpha and risk models.
• Designed or co‑designed new ML‑based components that fed directly into PnL improvements.Improved data pipelines and feature computation speed, increasing research efficiency.
• Helped reduce risk‑related drawdowns by identifying and correcting model sensitivities.

Motivations

• Seeking a more structured, high‑performance market‑making environment like Optiver.
• Wants to work with stronger PMs/traders and avoid bottlenecks introduced by inconsistent risk management.

Seniority level: Not Applicable

Employment type: Full‑time

Job function: Finance