Overview
At Tesco, our Data Science team focuses on modelling complex business problems and deploying data products at scale across physical stores, online, finance, and supply chain.
The team works across domains such as forecasting, online, pricing, security, fulfillment, distribution, property, IoT, and computer vision. Team members are encouraged to allocate time for learning and personal development, supported by multiple resources and academic collaborations.
Responsibilities
This is a hands‑on position where you will leverage your analytical mindset to find solutions to complex problems, prototype solutions with minimal support, and drive innovation. You will be expected to:
* Design and improve deep‑learning and related machine‑learning forecasting models for large‑scale retail time series.
* Build robust training and back‑testing pipelines (time‑aware CV, leakage controls, stability checks).
* Apply best practices for productionisation, including monitoring, retraining strategies, performance and cost considerations.
* Collaborate closely with engineering, product, and business partners to ensure models are reliable, scalable, and deliver measurable business value.
* Validate, document, and present modelling processes and performance, communicating complex solutions clearly to non‑experts.
* Promote data science across Tesco and represent the organisation at external data science community events.
* Take ownership of aspects of project development, support the Lead Data Scientist and Product Manager in managing stakeholder relationships, and mentor and supervise junior team members and interns.
Qualifications
* Ambitious individuals with a mix of statistics, programming skills, and familiarity with time‑series analysis.
* Proven track record of designing and modifying advanced algorithms and applying them to large data sets.
* Experience with project and stakeholder management (preferred).
* Strong scientific mindset, ability to ask and answer the right questions.
* Higher numerical degree in mathematics, science, engineering, or computer science (preferable).
* Solid understanding of mathematics and statistical principles.
* Meaningful academic or industrial work involving deep learning or related machine‑learning techniques for time‑series forecasting, including neural‑network architectures such as sequence models, temporal convolutional approaches, transformer‑style models, or related methods.
* Experience with probabilistic forecasting, hierarchical forecasting, or representation learning for time series (a plus).
* Strong programming skills, Python preferred.
* Familiarity with software engineering best practices: version control, unit testing, CI/CD.
* Experience and knowledge of big data and cloud technologies (PySpark preferred).
* Experience training and deploying deep learning models in cloud environments, including GPU‑enabled workflows (advantage).
We are committed to creating a fully inclusive and accessible recruitment process.
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