Descriptive Sales Data Scientist

I have a sizeable collection of sales data and I want to understand exactly what it is telling me before I move on to any forecasting or optimisation work. I am therefore looking for a quantitative researcher with a solid data-science toolkit who enjoys digging into datasets and communicating clear, descriptive insights.

The core of the assignment is a descriptive analysis: cleaning the raw sales tables, exploring distributions, segmenting by relevant dimensions (time, product line, geography, customer type, etc.) and turning those findings into well-structured visuals and concise commentary that a non-technical stakeholder can act on. Python or R is fine—as long as your code is reproducible and annotated—and I expect any dashboards or notebooks you build to run locally without extra licence costs.

Deliverables:
• A cleaned, well-documented dataset (CSV or Parquet)
• Fully reproducible analysis code (Jupyter Notebook or RMarkdown)
• Executive-ready report or slide deck summarising key metrics, trends and outliers
• A short hand-off call or Loom video walking through the results and how to update the analysis

I’m happy to provide a sample of the data ahead of time so you can get a sense of its structure. If you have experience explaining data stories to commercial teams and can turn this descriptive study around efficiently, let’s get started.