Location
London - Hybrid Working (Central London Farringdon office and home)

Hours
Full Time

Salary
Negotiable

About the Role
As a Data Scientist at Sainsbury's, you will play a pivotal role in developing in-house data science solutions that automate decision-making and provide valuable insights across the business. Working within cross-functional agile squads, you will contribute to the design and development of data science products, delivering high-quality analyses that guide future development and support continuous learning within the team. You will have access to extensive, clean, and well-documented data on an industry-leading platform, allowing you to focus on building impactful data science solutions rather than data cleaning.

Projects vary widely, including supply chain optimisation, predicting online shopper preferences, and supporting in-store colleagues to keep shelves stocked. You will be part of a large team of data scientists, engineers, and product managers, with opportunities for knowledge sharing, mentoring, and professional growth. The role supports hybrid working and offers a 10% colleague discount on Sainsbury's products, with up to 15% discount available for two days each week.

Experience
- Proven track record of delivering value-driving data science projects in a commercial environment.
- Proficient in writing production-grade code using Python and SQL.
- Strong statistical foundation including regression, hypothesis testing, and experimental design.
- Experience with sklearn and tree-based models.
- Highly proficient in Git best practices.
- Experience working in cloud environments such as Azure, AWS, or GCP.
- Strong presentation skills and business acumen, able to explain complex topics to non-technical audiences.
- Comfortable working independently using own initiative.

About you
Analytical, curious, and collaborative with a natural instinct for problem-solving. You thrive in a cross-functional team environment and are motivated by making a measurable impact through data and AI.

Qualifications
- Educated to degree level, preferably in a mathematical, statistical, or STEM discipline.
- Desirable: Post-graduate degree (Masters or PhD) in a relevant field.
- Desirable: Experience building or maintaining machine learning pipelines and endpoints in Azure ML.
- Desirable: Experience delivering ML projects in retail or supply chain contexts.
- Desirable: Knowledge of mathematical optimisation, causal inference methods, Graphical Neural Networks (GNNs), and other advanced models.
- Experience working within cross-functional teams is highly valued.