The Tech Data & AI function includes an Advanced Analytics SCM team, which works hand in hand with the Supply Chain organization on strategically relevant use cases that have the potential to deliver substantial value and redefine the way that the Supply Chain business function operates.
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
- 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)
- 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
- 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)
- 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
