Senior Data Scientist

Role Overview

You will own end to end model development for a defined workstream, with the room to shape the modelling approach rather than only execute someone else's. You will work directly with client stakeholders, translate ambiguous business questions into problems that can actually be modelled, and see your work through to a decision that gets made differently because of it. We keep teams small and senior, so the distance between your work and the decision it informs is short.

Technical Skills

• Languages & Core Libraries: Python (pandas, numpy, scikit-learn) and strong SQL. R is welcome as a secondary language.
• Machine Learning: Advanced depth in at least one area — ensembling, time series and forecasting, NLP, or computer vision. Solid applied statistics, hypothesis testing and causal inference fundamentals.
• Experimentation & Evaluation: Experiment design, A/B testing, offline and online evaluation, and the judgement to know when a metric is misleading you.
• MLOps: Model versioning, model registry, reproducibility, and experiment tracking with MLflow or an equivalent.
• Cloud: Hands on with at least one of AWS, GCP or Azure, including their managed ML services.
• Engineering Practice: Git, code review, testing, and the discipline to write analysis someone else can pick up six months later.

Good to Have

Not required, but a strong plus if your experience already includes any of the following.

• Domain exposure to retail, consumer goods, manufacturing or supply chain
• Experience with experimentation platforms and MLOps tooling
• Prior mentoring experience, formal or informal