Data Analyst to support intervention testing through data preparation, statistical analysis, and interpretation of results.
The role is primarily focused on hands-on analysis.
Key Responsibilities
- Prepare and clean datasets from multiple sources for analysis
- Conduct statistical analysis, including:
- Linear regression modelling
- Hypothesis testing (e.g. t-tests, correlation)
- Difference-in-differences analysis
- Analyse baseline metrics and intervention effects
- Produce clear, structured analytical outputs (tables, charts, summaries)
- Work with the project team to refine analyses and validate findings
Required Skills
Technical
- Python (core requirement):
- pandas, numpy for data preparation
- statistical testing (scipy/statsmodels or equivalent)
- regression modelling
- SQL for data extraction and dataset preparation
- Ability to:
- run and interpret statistical tests
- validate and check analytical outputs
- Advanced Excel: Strong analytical capability using Pivot Tables, advanced formulas (e.g. INDEX/MATCH, XLOOKUP, SUMIFS), and dynamic arrays. Experience using Power Query for data transformation and the Analysis Toolpak for regression and correlation to support exploratory and validation analysis.