Senior data scientist

This role is for someone who thrives on solving ambiguous, high value problems independently, taking a business question, shaping it into a data science approach, and owning it from start to finish through to production. You’ll work closely with a small team of data scientists and engineers, contributing technical depth and mentoring more junior colleagues along the way, but the core of the role is hands on delivery., * Model ownership from start to finish: Design, build, and deploy production grade machine learning models, from problem framing and experimentation through to live deployment and monitoring.

• Technical depth: Apply strong statistical modelling, ML, and (where relevant) NLP/LLM techniques to solve real business problems, not just exploratory analysis.
• Experimentation and measurement: Design and run experiments (A/B testing, causal inference) to validate impact and guide decision making.
• Cross-functional delivery: Partner closely with Product, Engineering, and MLOps teams to move ideas from prototype into scalable production systems.
• Mentorship: Support and coach junior data scientists on technical approach, code quality, and best practice, without formal management responsibility.
• Stakeholder communication: Translate complex technical findings into clear, actionable insights for non-technical stakeholders.
• Best practice: Contribute to how the wider data science function approaches tooling, methodology, and model governance.

Technical Environment

• Languages/Tools: Python (NumPy, Pandas, Scikit learn), deep learning frameworks (PyTorch/TensorFlow), SQL.
• MLOps: MLflow, cloud ML platforms (Azure ML, AWS SageMaker, or Databricks).
• Focus areas: Predictive modelling, recommender systems/personalisation, and increasingly, applied LLM/GenAI use cases, reflecting where the market is heading right now.
• Infrastructure: Cloud based (AWS/GCP/Azure, client dependent).

• Strong commercial experience as a data scientist, with a proven track record of shipping models into production, not just notebook-based analysis.
• Expert level Python and solid grounding in statistics/experimental design.
• Experience with recommender systems, personalisation, or ranking techniques is highly desirable.
• Exposure to LLMs, Generative AI, or conversational AI products is a strong plus given current market demand.
• Comfortable taking an unclear business problem and shaping it into a scoped data science approach, including recognising where ML isn’t the right answer.
• Strong communication skills, able to flex between technical depth and clear business-facing summaries.