ML Researcher

<h3>📋 Description</h3> • 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. • 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. <h3>🎯 Requirements</h3> • 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. • 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. <h3>🏖️ Benefits</h3> • 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.