Basildon, United Kingdom | Posted on 29/04/2026
Senior Data & Reporting Analyst
Start: ASAP
Contract Duration: 3 - 6 Months
Rate: Competitive Inside IR35
Location: Essex or Manchester, Hybrid Working Model
Deloitte is a leading global provider of audit and assurance, consulting, financial advisory, risk advisory, tax, and related services. With a global network of member firms in more than 150 countries and territories, we serve four out of five Fortune Global 500 companies. Our professionals are committed to making an impact that matters, delivering excellence in a challenging and dynamic environment. This is an exciting opportunity to support Deloitte Operations and work with a leading Tier 1 Banking client and support their better ways of working.
Role Overview
Drive MIPR's forecasting and modelling capability - building and maintaining the quantitative models that underpin scheme volume forecasts, customer response projections, and provision reporting - while also supporting the broader MI and regulatory reporting suite.
Technical Skills Required
- Python or R – essential; this role will be building and running statistical models, not just consuming outputs; comfortable writing reproducible, documented code for forecasting and modelling workflows
- Statistical modelling – direct experience of building predictive or probabilistic models in a financial services or regulatory context; specifically, response rate modelling, volume projection under uncertainty, and sensitivity analysis
- Customer response modelling – experience modelling consumer behaviour at scale; understanding of how response rates, opt-out rates, and engagement patterns are estimated and stress-tested across large populations with limited historical data
- SQL – essential; querying large datasets to extract the populations and behavioural data
- Excel – advanced; model outputs must be translatable into structured financial formats for Finance provision reporting and ExCo consumption
- Time series and scenario analysis – ability to build multi-scenario forecast models with defined assumptions, clearly communicated uncertainty ranges, and version-controlled outputs as assumptions evolve
- Power BI – ability to surface model outputs through MI dashboards, or similar, and reporting packs to a non-technical senior audience
- Data validation and model governance – structured approach to assumption documentation, model versioning, and output checking; able to articulate model limitations clearly to a regulatory audience if required
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