We are a prop-trading company that combines the agility of a startup with the resources of a high-performing fund. Our team is focused on developing cutting-edge strategies, and working with us means not just advancing technology, but also being part of a team where ideas are valued, professional growth is encouraged, and every member has the opportunity to unlock their full potential.
We’re looking for a Quantitative Researcher with a strong background in machine learning and time series modeling to join our team.
What You’ll Be Doing:
- Researching, developing, and deploying cutting-edge machine learning models for forecasting complex, high-dimensional time series — from market signals to macroeconomic indicators and alternative data.
- Building ML pipelines from scratch: data ingestion, feature processing, modeling, calibration, and monitoring.
- Designing custom validation and testing approaches for non-stationary data, including regime shift detection and adversarial evaluation.
- Working with large-scale data sources — tick-level, satellite, transactional, web-scraped — and transforming them into structured features.
- Collaborating with quants and engineers to integrate ML models into real-world investment processes.
- Contributing to strategic research initiatives, including causal inference, representation learning, and attention-based models for time series.
Requirements
Experience:
- 4–8 years of work experience, ideally a mix of academia and industry.
- Publications at top AI venues (NeurIPS, ICLR, ICML) in the fields of Time Series or Signal Learning.
- Experience building models that forecast market or alternative signals, macroeconomics, commodities, or sentiment.
- Participation in building an ML research culture: internal toolkits, mentorship, and open science practices.
Skills & Education:
- Expertise in deep learning for time series: Temporal Fusion Transformers, DeepAR, N-BEATS, PatchTST.
- Knowledge of causal inference and counterfactual reasoning for time series.
- Experience in multi-modal learning (time series + tabular data + text).
- Proficiency with the ML stack: PyTorch, HuggingFace, DVC, Docker, etc.
- Skills in model validation for non-iid data: custom cross-validation strategies, regime-aware data splits.
- Ability to build end-to-end ML pipelines — from data ingestion to production inference.
- Master’s degree or PhD in a quantitative field (Physics, Mathematics, Computer Science, or related areas).
- Languages: Russian, English.
Nice to have:
- Understanding of option pricing models, hedging.
- Experience with C++ or Rust.
- Ability to communicate technical ideas to diverse audiences, including non-technical stakeholders.
Benefits
- Culture of Innovation: An open, dynamic, and inclusive environment where your ideas matter.
- Flexibility & Impact: Enjoy the freedom of a startup with the backing of a well-resourced fund.
- High Impact: Work directly on projects that shape strategies and drive the fund’s success.
- 35 Days of Vacation – Plenty of time to rest and recharge.
- 100% Paid Sick Leave – Recover without financial worries.
- Top-Tier Equipment – Choose the tools that suit you best (within budget).
- Corporate Psychologist – Mental health support when you need it.