Senior Data Scientist
Listed on 2026-06-22
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IT/Tech
Machine Learning/ ML Engineer, Data Scientist, Data Analyst, AI Engineer (Applied/Software)
hackajob collaborating with Sainsbury's DTD to connect them with exceptional professionals for this role.
At Sainsbury’s, data sits at the heart of how we operate, innovate and serve our customers. Our Data & Analytics team is building a technically advanced, commercially focused and impactful capability, powering our Next Level Strategy and helping to create a Sainsbury’s powered by industry leading AI algorithms. We use data, technology and advanced analytics to drive better decisions across the business, from forecasting and optimisation to experimentation, personalisation and machine learning.
With one of the richest retail datasets in the UK and a portfolio spanning Sainsbury’s, Argos, Habitat and Nectar, the opportunity to innovate is huge. Here, you’ll tackle complex challenges at scale, create measurable impact and grow quickly alongside brilliant colleagues. People who thrive with us combine business understanding, technical expertise and curiosity, with a natural instinct for problem‑solving. Join us and help shape the future of retail through data and AI.
Data Scientist
- Hybrid Working
- London/Home What You'll Do
As a Senior Data Scientist and member of our Data Science team, you will play a pivotal role in developing in‑house data science solutions that automate decision‑making and provide valuable insights across our business. Working in cross‑functional agile squads, you will lead on the design and development of our data science products as well as contribute to the team's continuous learning and upskilling efforts.
WhyJoin Us
- Wide impact. Deploying models here means improving the experience of millions of customers each day.
- Range of projects. Our projects vary from supply chain optimisation for one of the largest logistics networks in the country, predicting which substitution products online shoppers prefer, to helping our instore colleagues keep shelves full.
- Focus on data science work. Access to extensive, clean, and well‑documented data in our industry‑leading platform: spend your time building data science solutions, not cleaning datasets.
- Time for growth. 10% of time set aside for learning & personal development.
- Learning and mentoring. With a team of 50 data scientists, engineers and product managers, there are lots of opportunities to learn from colleagues through knowledge shares, pair programming, and communities of practice.
- Flexible working. Our team prioritises hybrid working, both at home and in our central London Farringdon office.
- Save on groceries. 10% discount on products across Sainsburys (Up to 15% for two days each week!)
- Lead DS development projects as part of cross‑functional deliveries with colleagues from our allied teams:
Product, Analytics, Science,[JT1] and Engineering. - Own the design and delivery of large scale data science solutions.
- Provide SME knowledge to support a range of projects and business initiatives.
- Conduct code reviews and model our team’s technical standards.
- Identify opportunities for team upskilling.
- Take ownership of or lead one of our strategic initiatives: e.g. ML Ops, coding standards, training etc.
- Pair program with other data scientists.
- Educated to degree level, preferably within a mathematical, statistical or STEM discipline.
- A solid track record of individually contributing to value‑driving data science projects in a commercial setting.
- You have experience of writing production grade code using Python and SQL, as some of our systems involve real‑time inference that affect millions of customer transactions.
- Deep specialist knowledge in one or more data science domains that we work with. Currently these include: machine learning, causal ML, forecasting, optimisation, GNNs and search.
- Extensive experience deploying models in cloud environments (Azure, AWS, GCP).
- Solid presentation skills and business acumen, you can translate an unstructured business problem into a meaningful data science project.
- Strong statistical foundation in concepts such as regression, hypothesis testing and experimental design.
- Highly proficient in Git best practices.
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