Data Specialist - Data Scientist

Overview

In this Data Scientist role, you will turn complex data into practical insights that drive commercial value for Morrisons. You will work within the Data & Analytics team to build predictive models, optimise processes, and support strategic decisions using our Google Cloud data platform. You’ll combine mathematics, software development, and storytelling to solve real business problems and deliver measurable impact. You’ll also mentor teammates and promote data literacy to broaden data-driven thinking across the organisation.

Pay / Benefits

• 15% discount in store from day one
• Annual bonus scheme
• Generous holiday entitlement
• Market leading pension scheme and life assurance
• Private healthcare
• Aviva Digital GP

Responsibilities

• Build trusted relationships with internal stakeholders to understand priorities
• Solve complex business problems using applied maths, ML, and statistical modelling
• Own data science deliverables across data products and present outputs clearly
• Develop agile proofs-of-concept to showcase solutions to stakeholders
• Champion data literacy and mentor squad members in technical concepts
• Deliver fast, value-driven results for smaller analytical requests with operational impact
• Focus modelling on driving commercial returns (EBITDA improvement, sales, waste reduction, efficiency)
• Write efficient, scalable code to minimise cloud compute overheads in GCP
• Extract, manipulate, profile, and transform structured and unstructured data in GCP
• Design, build, test, and productionise ML models (sales/waste forecasting, segmentation, recommendations)
• Build feature sets and reusable data science pipelines with validation checks
• Apply causal inference and A/B testing to isolate true business impacts
• Uphold data governance, privacy, and ethical AI standards

Key requirements

• Programming in Python or R (or SQL/Java/C++) with clean, tested code
• Strong knowledge of descriptive and inferential statistics
• Experience building, validating, and productionising ML models
• Advanced SQL skills for large-scale data profiling
• Understanding of causal inference and experimentation methods
• Ability to translate outputs into dashboards and executive recommendations (Looker)
• Understanding of data ethics and privacy in modelling
• Experience with Agile or Waterfall delivery and MVP scoping

• Data storytelling
• Stakeholder management
• Mentoring and coaching
• Python or R programming
• Statistical methods (descriptive/inferential statistics, hypothesis testing)
• Machine learning methods (linear/logistic regression, time-series forecasting, decision trees)