Full-Time Quantitative Researcher (MFT / ML Pipeline)

• Assist in building and maintaining the machine learning and feature engineering pipeline for MFT trading strategies.
• Conduct data-driven market research to identify patterns, features, and signals relevant to short- to medium-term alpha generation.
• Contribute to model training, validation, and performance analysis across diverse datasets (e.g., equities, futures).
• Implement proof-of-concept trading models and help backtest hypotheses using large-scale historical data.
• Collaborate closely with senior quants, data engineers, and infrastructure teams to ensure efficient data flow and model deployment.
• Continuously evaluate model performance, adapt to evolving market conditions, and propose refinements.

Requirements:

• 1–2 years of experience in a quantitative, research, or data science role (finance, trading, or applied ML preferred).
• Strong understanding of machine learning workflows (data preparation, feature generation, model validation).
• Good grasp of statistics, time-series analysis, and predictive modeling.
• Working knowledge of Python and common data libraries (Pandas, NumPy, Scikit-learn, PyTorch, etc.).
• Familiarity with financial data structures (tick data, OHLCV bars, order book/L2 data) is a plus.
• Strong analytical and problem-solving skills, with attention to detail.
• Ability to work both independently and collaboratively in a distributed team.
• Clear communicator with a genuine interest in markets and applied research.

Nice to have:

• Master’s degree in a quantitative field (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar).
• Exposure to quantitative trading or research environments (internships or projects welcome).
• Experience with feature pipelines, MLOps frameworks, or data versioning tools.
• Interest in market microstructure, feature selection, or model-based execution.
• Familiarity with C++ or Rust is a plus.

What is the last date for applying to the job?

Which countries are accepted for this remote job?