Senior Data Scientist

Overview

As a Senior Data Scientist, you will lead advanced analytics projects using predictive and prescriptive methods on a rich linked health dataset. You will work closely with leadership to translate complex analyses into production tools that inform operations, strategy, and population health decisions. You’ll ensure ethical use of data to avoid health inequalities and promote responsible innovation. The role blends hands-on modeling with collaboration across NHS partners and analytics teams to deliver tangible impact and scalable insights.

Responsibilities

• Independently develop data science solutions oriented to actionable insights and tools, considering data quality, user needs, and deployment.
• Use open-source tools (R or Python) with Git and follow RAP standards for reproducible analytics.
• Maintain broad knowledge of advanced statistical methods and align modelling specialism with seniority.
• Collaborate with stakeholders to understand business needs and co-design project plans across problem articulation, modelling, deployment, and evaluation.
• Communicate complex findings with clear recommendations for tactical and strategic decisions.
• Identify personal development opportunities and adopt innovative practices from the analytics community.
• Mentor analysts to develop technical skills within the team.
• Uphold high ethical standards across the data science lifecycle

Key requirements

• Masters level education or equivalent analytical experience
• Expert in Python/R
• Experience with simulation methods (Discrete Event, System Dynamics, MCMC) or regression/GLM (logistic/Poisson), multilevel models, ANOVA
• Bayesian modelling, causal inference, ML/predictive models (trees, random forests, boosting, neural networks)
• Experimental design and appropriate analytics testing
• Experience with reproducible analytics pipelines (Git, GitHub, documentation, automation, code review)
• Experience supporting team development
• Desirable: model deployment and monitoring tools, automation of reporting, project management approaches (Prince2, CRISP-DM, Agile)
• Ability to explain analytical principles to technical and non-technical audiences
• Strong stakeholder engagement and decision-support capabilities

• Collaborative mindset
• Strong communication and storytelling
• Problem solving under changing timescales
• Python
• R
• Discrete Event Simulation

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