Senior Data Scientist - Customer & Loyalty Analytics

Job description

Senior Data Scientist - Customer & Loyalty Analytics – Hybrid Working London/Home

We have ~90 Analysts and Data Scientists within Customer & Loyalty Analytics focusing on a wide range of business questions, and we have millions of known customers to play with. This team is focussed on building our personalised customer decisioning engine, known internally as Krang. We are the brain behind an optimised selection of offers, mechanics and discounts used to drive desired customer behaviour. We do some of Sainsbury’s most complex modelling, forecasting, tool building, and insights-generation and use agile working methodologies to make sure we’re prioritising the right outcomes. Our Data Scientists are critical in supporting Sainsbury’s to be successful in our corporate strategy of Connecting to our Customers. As a Senior Data Scientist, you will be designing and evolving personalised decisioning to drive both customer loyalty and commercial value.

What will you be doing?
Specifically, this Senior Data Scientist role will be supporting the team in the following: 

• Lead on the technical development of algorithms and pipelines that will deliver against Krang’s strategic objectives.
• Iterate our modelling and optimisation capabilities, identifying the most appropriate techniques to enable continuous learning, expansion to novel mechanics, audiences and channels and the transition towards real-time decisioning.
• Ensure the analytical models we develop and deploy align to engineering principals around efficiency, on-demand, and automation.
• Become the recognised expert in Data Science techniques used for decisioning and be opinionated on future approaches, including state of the art techniques where appropriate.
• Support Junior Data Scientists in defining projects and analytical approaches, as well as reviewing their code
• Willing to lead a sub-team on tactical projects working with stakeholders to define goals and Junior Data Scientists to deliver impact at pace
• You will demonstrate store closeness and support our stores during peak trading periods
• You will actively contribute to our vibrant Data and Analytics community of over 800 colleagues, providing a view on new techniques and approaches that can drive positive change in wider teams.

What are we looking for?

• Extensive programming ability across Python and strong ability to use SQL, with a proven experience of developing complex solutions in a corporate environment. 
• Excellent skills and statistical foundation in concepts such as linear optimisation, predictive modelling, clustering and time series analysis. 
• Experience designing and implementing price optimisation problems is desirable but not essential. 
• Knowledge and experience of: model building, statistical analysis, hypothesis generation, experiment design and execution, either gained through an advanced quantitative degree, or equivalent practical experience in an industry setting.
• Experience using cloud platforms such as AWS or Azure and associated ML tools is desirable but not essential. 
• Experience using Jupyter notebooks and version control within a team using Git. 
• Business acumen and commercial awareness.
• Ability to articulate required outcomes and present analytics work succinctly. 
• Independence to achieve results and work under your own guidance and initiative
• Able to lead technical conversations with data engineers. 

Essential Criteria

• Demonstrable ability to develop and maintain advanced data science solutions using Python and SQL within complex production or corporate environments.
• Proven capability in applying statistical and machine‑learning techniques such as optimisation, predictive modelling, clustering, and time‑series analysis to real business problems.
• Evidence of designing, building and deploying analytical models or pipelines that align with engineering principles for efficiency, automation and scalable delivery.
• Ability to lead technical discussions and provide guidance to colleagues, including reviewing code, supporting analytical project design, and collaborating effectively with data engineers.
• A strong business acumen and commercial awareness