Quantitative Python Developer

IMC's Python Development team partners directly with our equity and index options trading desks, blending financial theory, software engineering, data-visualisation, and applied research to convert raw data into trading edge. You'll operate in a high-calibre, intellectually stimulating environment that leverages cutting-edge technology, proprietary tools, and vast datasets. Working side-by-side with traders and software engineers, you'll see your work move swiftly from idea to production and directly influence trading performance. Our culture prizes innovation, collaboration, and continuous learning-where creative problem-solving, a strong sense of responsibility, and rigorous diligence drive success.

Your Core Responsibilities:

• Collaborate closely with traders to refine existing strategies and generate new ideas.
• Build, maintain, and enhance research frameworks and data pipelines that enable trading and quantitative research.
• Develop, back-test, and implement discretionary and systematic trading strategies using large, diverse datasets.
• Curate, transform, and present data in clear, accessible formats for traders and researchers.
• Drive end-to-end research cycles - from ideation through production deployment.

Your Skills and Experience:

• Bachelor's in Mathematics, Physics, Statistics, Computer Science, Econometrics, or a related discipline.
• 3+ years of professional Python development experience, with strong, production-level coding skills; familiarity with additional languages is a plus.
• Hands-on experience with statistical analysis, numerical programming, data engineering, or machine learning in Python (Polars, Pandas, NumPy, SciPy, TensorFlow).
• Proven ability to handle large datasets, architect and optimise data pipelines, and present data effectively.
• Foundational knowledge of quantitative trading concepts and equity/index options - whether through coursework, projects, internships, or professional work.
• Exposure to web/API frameworks such as FastAPI and React is advantageous.
• Experience with orchestration and containerisation tools like Kubernetes and Docker is a plus.
• Solid grounding in calculus, probability, statistics, and optimisation; familiarity with machine-learning techniques is beneficial.