The SCM Advanced Analytics team builds data science products to make supply chain faster, more efficient, and responsive through seamless demand & supply planning, optimized logistics and distribution, customized replenishment, and automated workforce and production planning.

The Assistant Data Scientist is a hands-on role within the SCM Advanced Analytics team, supporting use cases related to Global & Market – Distribution and Outbound excellence. Working under the guidance of a Data Scientist or Senior Data Scientist, the role contributes to the data preparation, analysis, modelling, and testing that sit behind our data science products – and builds, over time, the technical depth and business understanding needed to take ownership of a use case.

Hands-on analytical delivery (under guidance)

• Perform data extraction, cleaning, profiling, and exploratory analysis in support of active use cases
• Build and test analytical components – features, model candidates, calculation logic, validation scripts – against requirements agreed with the responsible Data Scientist
• Write clear, readable Python and SQL, following team standards on version control, code review, and documentation from day one
• Investigate data quality issues, trace them back to source systems, and propose fixes
• Prepare validation outputs, back-tests, and comparison analyses that show whether a solution performs as intended
• Support solutions running in production: run scheduled checks, flag anomalies, and help reproduce and root-cause issues
• Produce analysis outputs – notebooks, charts, summary tables, dashboard components – that colleagues can pick up and re-run without explanation

Stakeholder collaboration & communication

• Join working sessions with DC, Outbound, and planning stakeholders; capture requirements, assumptions, and open questions in a structured way
• Ask questions to understand the operational process behind the data rather than taking fields and figures at face value
• Present analysis results to the project team and working-level business contacts clearly and honestly, including what the analysis does not show
• Support user testing and enablement sessions; collect user feedback and log it for follow-up
• Keep the responsible Data Scientist and the Product Owner informed on progress and blockers early

Learning & ways of working

• Actively build technical depth in the team’s core stack and working knowledge of distribution centre and outbound processes
• Seek out and apply feedback on code and analysis; treat code review as a learning channel
• Work in agile delivery cycles: manage own tasks, estimate honestly, and raise blockers early
• Contribute to team documentation, reusable components, and knowledge sharing
• Ensure compliance with relevant statutory or external regulations and codes of good practice

This role carries no direct people management responsibility.

• Data Scientists and Senior Data Scientists within SCM Advanced Analytics (day-to-day guidance)
• Director Product Ownership – SCM Advanced Analytics (line manager)
• Global & Market SCM teams – Distribution Centre operations and planning teams
• Data engineers and other Advanced Analytics teams
• Other teams within Tech (e.g. Data Platforms & Data Governance)

Requisite Education and Experience / Minimum Qualifications

Education

• University degree (Bachelor’s or Master’s) in a quantitative discipline (Computer Science, Data Science, Statistics, Mathematics, Physics, Econometrics, Operations Research, Industrial or Supply Chain Engineering, or comparable)

Work experience

• 2 – 4 years of professional experience in a data, analytics, or engineering role; relevant internships, working student positions, or a substantial applied thesis project are equally valid
• Any exposure to supply chain, logistics, manufacturing, or retail operations is a plus, but is not a requirement

Hard skills

• Working proficiency in Python and SQL, demonstrated through academic projects, internships, or personal work
• Sound grounding in statistics and core data science methods (regression, classification, clustering, basic time-series)
• Familiarity with version control (Git) and comfort working in a notebook environment
• Exposure to cloud data or lakehouse platforms (Databricks, Spark/PySpark) is an advantage – training will be provided
• Able to visualise data and present results clearly; experience with a BI or app framework is a plus
• Fluent English (written and spoken)

Soft skills

• Clear and to-the-point written and oral communication skills (English)
• Genuine curiosity – asks why the numbers look the way they do, and follows the question through
• Intellectually honest: reports what the data supports and flags uncertainty rather than smoothing over it
• Structured, reliable, and accountable for agreed tasks and deadlines
• Eager to learn quickly, act on feedback, and work hands-on with operations teams, including on site
• Resilience and a solution-oriented attitude