About this role

Own end-to-end model development across client engagements, from data exploration and feature engineering through to model deployment and ongoing optimisation
Work on a variety of high-stakes AI applications, often simultaneously, in environments where rigour and speed both matter
Communicate clearly with clients, walking them through methodology or explaining model outputs to non-technical stakeholders with equal confidence.

3+ years of hands-on data science experience, with a strong portfolio of production ML models
Deep proficiency in Python and the ML stack (scikit-learn, XGBoost, PyTorch, or TensorFlow)
Experience in at least one of: credit risk / underwriting, fraud detection, NLP, recommendation systems, or computer vision
Strong grasp of statistical fundamentals, you understand what your model is actually doing
Experience deploying models in cloud environments (AWS, GCP, or Azure) and with MLOps practices
Ability to work directly with clients and translate messy real-world data problems into clean modelling approaches
Experience with LLMs, RAG pipelines, or generative AI applications is a strong advantage.

Data Scientist

Key skills