Data Scientist

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.

As a 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 contribute to the design and development of our data science products. You will deliver high-quality analyses that guide future development work and contribute to the team's continuous learning and upskilling efforts.

• 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 prioritizes 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!).

What you need to excel in this role:

• 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.
• Strong presentation skills and business acumen, you regularly receive positive feedback about your ability to explain complex topics to non-technical audiences.
• You can feel comfortable working independently to achieve results using your own guidance and initiative.
• Strong statistical foundation in concepts such as regression, hypothesis testing, experimental design.
• Experience getting the best out of sklearn and tree-based models.
• Highly proficient in Git best practices.
• Experience working in cloud environments (Azure, AWS, GCP).
• A relevant Post-graduate degree (Masters or PhD) within a mathematical, statistical or other STEM discipline.
• Experience building or maintaining machine learning pipelines and endpoints in Azure ML.
• Experience delivering value on ML projects in retail (in areas such as price elasticity or differential pricing) or supply chain (demand forecasting, optimisation etc.).
• Experience going from unstructured business problems through to well defined Data science solutions.
• Our products often utilize mathematical optimisation, so experience in this area is highly desirable.
• Experience in applying causal frameworks to data science solutions and applying causal inference methods.
• Experience working with and deploying Graphical Neural Networks (GNNs) in industry.
• Experience with other more complex models and frameworks.
• Experience working within a cross functional team.

We are committed to being a truly inclusive retailer so you’ll be welcomed whoever you are and wherever you work. Around here, there’s always the chance to try something new — whether that’s as part of an evolving team or somewhere else across the business - and we take development seriously and promise to support you. We also recognise and celebrate colleagues when they go the extra mile and, where possible, offer flexible working. When you join our team, we’ll also offer you an amazing range of benefits.

Starting off with colleague discount, you'll be able to save 10% on your shopping online and instore at Sainsbury's, Argos, TU and Habitat, and we regularly increase the discount to 15% at points during the year. We've also got you covered for your future with our pensions scheme and life cover. You'll also be able to share in our success as you may be eligible for a performance-related bonus of up to 10% of salary, depending on how we perform.

Your wellbeing is important to us too. You'll receive an annual holiday allowance and you can buy up to an additional week's holiday. We also offer other benefits that will help your money go further such as season ticket loans, cycle to work scheme, health cash plans, salary advance (where you can access some of your pay before pay day) as well access to a great range of discounts from hundreds of other retailers. And if you ever need it there is also an employee assistance programme.

Moments that matter are as important to us as they are to you which is why we give up to 26 weeks’ pay for maternity or adoption leave and up to 4 weeks’ pay for paternity leave.

Please see www.sainsburys.jobs for a range of our benefits (note, length of service and eligibility criteria may apply).