Senior Quantitative Researcher in London

We are a prop-trading firm that blends the agility of a startup with the capabilities of a high-performing fund. We build advanced, data-driven trading strategies across asset classes, and foster a culture where ideas matter, ownership is encouraged, and every team member can unlock their full potential.

We are looking for a Senior Quantitative Researcher with deep expertise in machine learning for time series, strong quantitative intuition, and hands-on experience developing models that drive real-world trading. Experience in options trading (commodities/metals/energy/crypto) is a significant plus.

What you will be doing:

• Develop and deploy state-of-the-art ML models for forecasting complex, high-dimensional, non-stationary time series (market microstructure, macro data, alternative signals).
• Build full ML pipelines from scratch: data ingestion, feature engineering, modeling, calibration, monitoring.
• Design advanced validation frameworks for non-IID and regime-shifting datasets.
• Work with large-scale, multi-modal datasets (tick, satellite, transactional, scraped, text).
• Formulate, test, and refine investment hypotheses within risk constraints.
• Build and enhance factor models, risk models, and return/volatility forecasting models across asset classes.
• Participate in portfolio optimization, accounting for transaction costs, market impact, and constraints.
• Conduct attribution analysis, stress testing, scenario modeling, and prepare insights for the Investment Committee.
• Develop advanced options strategies, volatility structures, and hedging frameworks in commodities and alternative assets.
• Analyze pricing anomalies, build volatility models (SABR, Heston, local vol), and construct bespoke exposures.
• Contribute to internal options analytics libraries (Greeks, volatility surface, PnL attribution, scenario analysis).

Requirements:

• 7+ years in quantitative research, systematic trading, or ML-driven modeling (mix of industry + academia is ideal).
• Publications in top AI venues (NeurIPS, ICLR, ICML) are a strong plus.
• Demonstrated experience building models that forecast market, macro, or alternative data signals.
• Proven involvement in institutional investment processes (risk, IC meetings, compliance).
• Track record of deploying live strategies or predictive models that delivered measurable performance.
• (Optional) 5–7+ years in commodities/options trading with exposure to metals/energy or alternative assets.

Skills & Education:

• Expertise in cutting-edge deep learning for time series, including State Space Models (S4/S5, Hyena, Mega), advanced Transformers (iTransformer, TimesNet, TimeGPT-style models), neural SDE/ODE architectures for high-frequency data, diffusion-based forecasting models, etc.
• Strong statistical foundations: regime-switching models, copulas, volatility forecasting.
• Experience with multi-modal learning (time series + tabular + text).
• Proficiency in Python, PyTorch, HuggingFace, DVC, Docker; C++/Rust is a plus.
• Ability to design custom validation strategies for non-IID datasets.
• Understanding of portfolio optimization with linear & nonlinear constraints.
• Master's or PhD in Physics, Mathematics, CS, or another quantitative discipline.

Languages: Russian, English.

Nice to Have:

• Knowledge of options pricing, stochastic volatility models, and hedging techniques.
• Experience with ML/DL/RL applied to trading.
• Ability to communicate complex ideas to both technical and non-technical audiences.

Benefits:

• Culture of innovation — a genuinely open, research-driven environment where curiosity is rewarded and your ideas directly shape real trading strategies.
• True flexibility — work from anywhere; we care about outcomes, not where or when you sit at your desk.
• High autonomy & ownership — no micromanagement, no bureaucracy. You get full responsibility over your research direction, models, and production impact.
• Startup agility, Fund resources — fast decision-making, minimal red tape, and access to the data, compute, and infrastructure you need to run serious research.
• Massive data advantage — work with a uniquely rich multi-modal dataset (order log, options chains, satellite data, alt-data, Bloomberg, proprietary feeds).
• Top-tier equipment — choose the hardware/software setup that makes you most productive.
• Well-being support — 35 days of vacation, 100% paid sick leave, and access to a corporate psychologist.
• Real career growth — shape research culture, lead initiatives, and influence long-term strategy directions.