Quantitative Python Developer Tiel • IMC Trading

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IMC’s Quantitative 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:

• Master’s or Ph.D. in Mathematics, Physics, Statistics, Computer Science, Econometrics, or a related discipline.
  
• 2–3 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.