At Tesco, our Data Science team builds scalable solutions to complex business challenges across stores, online, supply chain, marketing and Clubcard. We apply advanced machine learning, generative AI, and large language models (LLMs) to personalise customer experiences, optimise operations and drive innovation. We work across several business domains, including customer experience, online, fulfilment, distribution, commodities, store operations and technology. Team members rotate across domains to broaden their expertise and impact.
We foster a culture of continuous learning, dedicating 10% of the working week to personal development. Our team benefits from academic partnerships, regular knowledge-sharing events and a collaborative, inclusive environment that values work-life balance and professional growth.
- Annual bonus scheme of up to 20% of base salary
- Holiday starting at 25 days plus a personal day (plus Bank holidays)
- Private medical insurance
- 26 weeks maternity and adoption leave (after 1 years’ service) at full pay, followed by 13 weeks of Statutory Maternity Pay or Statutory Adoption Pay, we also offer 4 weeks fully paid paternity leave
- Free 24/7 virtual GP service, Employee Assistance Programme (EAP) for you and your family, free access to a range of experts to support your mental wellbeing
We are seeking an AI Data Scientist with a strong foundation in large language models (LLMs) and a pragmatic approach to real-world AI applications. The role involves fast-paced prototyping, researching emerging tools and technologies, creating benchmarks, setting standards, and contributing directly to production code and fully fledged products. While existing knowledge is valuable, a strong ability to learn quickly and apply new skills effectively is essential. The ideal candidate will be solution-oriented, eager to stay current with the latest developments, and comfortable in a fast-paced environment with ample room for creativity and problem-solving.
- Broad understanding of LLM architectures, training methodologies, and usage patterns.
- Practical experience applying LLMs, including:
- Managing context windows effectively
- Selecting appropriate models for specific tasks
- Implementing safety guardrails and alignment techniques
- Decomposing complex tasks into model-friendly components
- Strong experience in evaluating and validating data pipelines and ML systems.
- Familiarity with AI-specific evaluation methods, including both quantitative metrics and qualitative assessments.
- Ability to make well-reasoned decisions grounded in technical understanding and real-world constraints.
- Pragmatic approach to experimentation and solution design.
- Actively engaged in learning and staying current with developments in AI and machine learning.
- Curious, adaptable, and committed to continuous improvement.
- Focused on delivering practical, scalable, and responsible AI solutions.