Senior Data Scientist

About the Role

The Food Data Science team is seeking a Senior Data Scientist to play a crucial role in transforming and optimizing the supply chain of Marks and Spencer’s award-winning food business. This role will significantly impact the profitability of the business and contribute to environmental and social responsibilities by minimizing food waste. You will bring energy and creativity to the transformation and optimisation of our Forecasting, Ordering and Allocation (FOA) capabilities.

Responsibilities

• Work as part of a team to produce well-founded statistical analyses to maximise the value from third-party systems.
• Create machine learning models to transform and optimise FOA capabilities.
• Develop a deep knowledge of the supply chain.
• Collaborate closely with colleagues within the Food Data Science team, Supply Chain FOA team, and the Data Engineering and Data Quality teams.

The Team

The Food Data Science team is collaborative and multi-disciplinary with high levels of skill in statistical analysis, trialling, and development of machine learning models. All work has a direct and significant impact on business performance. We work closely with business and technical teams across the firm, placing significant emphasis on learning and development in technical, commercial, personal, and interpersonal skills. We value diversity of background and experience to apply multiple perspectives to our work.

Technical Requirements

• Secure foundations in linear algebra, calculus, and probability.
• Experience of, and enthusiasm for, using statistical methods to solve commercial problems.
• Experience of, and enthusiasm for, creating machine learning models based on insights from statistical analyses.
• Skilled in one or more programming languages; we use SQL, Python, and Scala.

Personal Skills

• Ability to communicate commercial, statistical, and machine learning insights to both technical and non-technical colleagues.
• Analytical curiosity and enthusiasm for problem solving to drive commercial transformation.
• Desire to work collaboratively in a highly skilled multi-disciplinary team.
• Growth mindset and enthusiasm for both learning and helping others to learn.
• Ability to build consensus and organise work collaboratively.

Ideal Requirements

• Academic background in Mathematics, Statistics, Statistical Machine Learning or closely related subject.
• Knowledge and understanding of Bayesian inference, the EM algorithm, variational approximation methods, inference on a graph including message-passing algorithms.