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