Job Description

We are seeking a talented and driven ML Quantitative Researcher to join our team to build and enhance a comprehensive automated research framework and pipeline. This role is integral to our mission of developing innovative and effective trading strategies.

Key Responsibilities:

• Feature engineering, combination and monetization;
• Research and develop a robust library of predictors and features;
• Research and design novel predictors, leveraging machine learning techniques and neural networks;
• R&D of linear and non-linear combination of features;
• Hyper-parameter optimization and monetization of forecasts;
• Execution and alpha for forecast monetization and risk management;
• Post-trade research;
• Collaborate with the trading team to integrate predictors into taking and market making strategies;
• Analyze and optimize multi-horizon HFT and MFT trading strategies;
• Conduct rigorous backtesting and validation of predictive models;
• Continuously monitor and improve the performance of existing predictors and the whole calibration pipeline;
• Research and monetize different asset classes and markets.

Qualifications

Requirements:

• Proven experience in research, development and monetization of predictors
• Knowledge of machine learning techniques (experience with neural networks is a plus);
• Understanding and experience with taking or market making trading strategies;
• Proven track record of working with HFT or MFT strategies;
• Strong programming skills in Python, C++ or Rust. Proficiency in other programming languages is a plus.

Preferred Qualifications:

• Advanced degree (PhD or Master's) in a quantitative discipline such as Computer Science, Statistics, Mathematics, Physics or a related field;
• Experience with statistical analysis, data mining, and predictive modeling;
• Proven track record of several profitable strategies in HFT/MFT;
• Strong problem-solving skills and the ability to work both independently and as part of a team;
• Excellent communication skills, with the ability to clearly explain complex concepts to non-specialists.