IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC. ย The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism.ย This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models.
Your Core Responsibilities:
- Design and deploy machine learning models to enhance trading performance across various asset classes
- Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
- Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models
- Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources
Your Skills and Experience:
- PhD or Master's in Engineering, Math, Statistics, Computer Science, or related quantitative field
- 2+ years of experience building applied ML models; previous experience in trading environment preferred
- Proven expertise in developing and deploying predictive models
- Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
- Strong understanding of theoretical foundations of state-of-the-art ML models
- Strong publication track record at ICML, ICLR, NeurIPS, or equivalent
- Ability and desire to work in a collaborative team environment
- Excellent written and verbal communication skills