Junior ML Quantitative Researcher

We are a proprietary algorithmic trading firm. Our team manages the full trading cycle, from software development to creating and coding strategies and algorithms. The firm trades across a broad range of asset classes, including equities, equity derivatives, options, commodity futures, rates futures, etc. We employ a diverse and growing array of algorithmic trading strategies, utilizing both High- and Medium-Frequency Trading approaches.

We are looking for an ML Quant Researcher to bridge our machine learning research and live trading operations. You will work at the intersection of ML output and trading strategy—understanding what the ML team produces, translating it into actionable signals, and combining feature sets with trading strategies to improve alpha generation. This role is not about deep learning research; it is about applied ML in a trading context, combining analytical thinking with a trader's intuition. The ideal candidate is someone who can work independently, takes initiative, and is genuinely curious about markets.

Key Responsibilities

• Serve as the link between ML research output and the trading team: monitor, understand, and translate ML model outputs into trading-relevant signals and features.
• Combine ML-derived features with existing trading strategies to improve performance and generate alpha across markets (CME, Japan, China).
• Evaluate feature sets, run experiments, and assess the practical impact of ML signals on strategy PnL.
• Collaborate with the trading team to understand strategy requirements and identify where ML can add edge.
• Track signal quality over time; identify degradation, overfitting, or regime changes and propose corrective actions.
• Contribute to strategy research and the development of new alpha sources.

Qualifications

• 1–2+ years of experience in quantitative research, ML in trading, or a closely related role.
• Solid applied ML skills—classical methods (regression, classification, ensembles, feature engineering) are sufficient; deep learning is not required.
• Ability to combine feature sets and trading strategies at a higher level of abstraction—thinking in terms of alpha, signal quality, and PnL impact.
• Strong Python skills; experience with data analysis and modelling libraries (TensorFlow as a must, pandas, scikit-learn, etc.).

We Offer

• An opportunity to work in a modern IT company with a strong engineering culture.
• Fully remote work from anywhere in the world, on a flexible schedule.
• Compensation for health insurance, sports, professional development, and more.