ML models into production. Prior experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps More ❯
trading constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like More ❯
and building reusable analytical datasets. Modern BI: hands-on experience with a modern BI platform with integrated semantic layer (e.g Omni, Looker, Thoughtspot) Python for analysis: practical experience with pandas/numpy and notebook-based workflows for LTV, attribution, simulations and experimentation analysis. Domain expertise: deep, practical knowledge in one or more of the following functions & platforms: CRM (e.g. SalesForce More ❯
