About the role
At Tesco, our Data Science team focuses on modelling complex business problems and deploying data products at scale. Our work extends across multiple areas including physical stores, online, finance, and supply chain. The Data Science team works across several domains and problem types: forecasting, online, pricing, security, fulfilment, distribution, property, IoT and computer vision. Our team members are encouraged to allocate working hours for learning and personal development every week and are provided with multiple learning resources and tools. Multiple academic collaborations enrich the team’s expertise; knowledge‑sharing events are held regularly. We offer a great work‑life balance, regular team days and a relaxed yet engaging culture.
Responsibilities
This is a hands‑on position where you will need to leverage your analytical mindset to find solutions to complex problems. As a Data Scientist, you will need to understand difficult business problems and prototype solutions with minimal support. A core component of the role is applying, modifying and designing algorithms and mathematical models to solve business problems using big data architectures (Hadoop, Spark, Cloud). In this role, your primary modelling focus will be neural‑network‑based forecasting, with a strong emphasis on rigorous evaluation against robust baselines, careful validation, and scalable deployment. You will validate, document and present the modelling process and performance, and communicate complex solutions in a clear, understandable way to non‑experts.
- 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 (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.
Qualifications
We are looking for ambitious individuals with a mix of statistics, programming skills and familiarity with time‑series analysis. A proven track record of designing and modifying advanced algorithms and applying them to large data sets is essential. Some project and stakeholder management experience is preferred. A strong numerical higher degree in a mathematical, scientific, engineering or computer science discipline is preferable. Meaningful academic or industrial work involving deep learning or related machine learning techniques for time‑series forecasting is required. This includes experience with neural‑network architectures for forecasting (e.g. sequence models, temporal convolutional approaches, transformer‑style models). Experience with probabilistic forecasting, hierarchical forecasting or representation learning for time‑series is a plus. Strong programming skills are essential (Python is preferred) as well as familiarity with software engineering best practices (version control, unit testing, CI/CD) and big data and cloud technologies (PySpark preferred). Experience training and deploying deep learning models in cloud environments, including GPU‑enabled workflows, is a strong advantage.
Benefits
- 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 (12 months service required at the qualifying date) at full pay, followed by 13 weeks of Statutory Maternity Pay or Statutory Adoption Pay, and 6 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.
- This information is a shortened summary; refer to our policies for full details.
About the role
At Tesco, our Data Science team focuses on modelling complex business problems and deploying data products at scale. Our work extends across multiple areas including physical stores, online, finance, and supply chain. The Data Science team works across several domains and problem types: forecasting, online, pricing, security, fulfilment, distribution, property, IoT and computer vision. Our team members are encouraged to allocate working hours for learning and personal development every week and are provided with multiple learning resources and tools. Multiple academic collaborations enrich the team’s expertise; knowledge‑sharing events are held regularly. We offer a great work‑life balance, regular team days and a relaxed yet engaging culture.
Responsibilities
This is a hands‑on position where you will need to leverage your analytical mindset to find solutions to complex problems. As a Data Scientist, you will need to understand difficult business problems and prototype solutions with minimal support. A core component of the role is applying, modifying and designing algorithms and mathematical models to solve business problems using big data architectures (Hadoop, Spark, Cloud). In this role, your primary modelling focus will be neural‑network‑based forecasting, with a strong emphasis on rigorous evaluation against robust baselines, careful validation, and scalable deployment. You will validate, document and present the modelling process and performance, and communicate complex solutions in a clear, understandable way to non‑experts.
- 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 (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.
Qualifications
We are looking for ambitious individuals with a mix of statistics, programming skills and familiarity with time‑series analysis. A proven track record of designing and modifying advanced algorithms and applying them to large data sets is essential. Some project and stakeholder management experience is preferred. A strong numerical higher degree in a mathematical, scientific, engineering or computer science discipline is preferable. Meaningful academic or industrial work involving deep learning or related machine learning techniques for time‑series forecasting is required. This includes experience with neural‑network architectures for forecasting (e.g. sequence models, temporal convolutional approaches, transformer‑style models). Experience with probabilistic forecasting, hierarchical forecasting or representation learning for time‑series is a plus. Strong programming skills are essential (Python is preferred) as well as familiarity with software engineering best practices (version control, unit testing, CI/CD) and big data and cloud technologies (PySpark preferred). Experience training and deploying deep learning models in cloud environments, including GPU‑enabled workflows, is a strong advantage.
Benefits
- 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 (12 months service required at the qualifying date) at full pay, followed by 13 weeks of Statutory Maternity Pay or Statutory Adoption Pay, and 6 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.
- This information is a shortened summary; refer to our policies for full details.
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