Description
- Evaluation design and execution. Design and run rigorous analyses (including coverage rates, incremental lift, etc.) for current external data sources across bureau, open banking, and alternative data categories. Define the right population, baseline, methodology, and success criteria for each evaluation in partnership with the strategy team before analysis begins.
- Incremental value measurement. Measure the marginal contribution of each data source over Client's existing data signals. Champion-challenger testing, population swap analysis, and coverage gap quantification are core tools.
- Retrospective analytics. Build and maintain the analytical framework that tracks post-integration performance for every data source in the portfolio. At six and twelve months post-launch, answer the question: did this source deliver the lift the evaluation projected, and does it continue to clear the performance threshold that justifies its cost?
- Coverage and population analysis. Quantify where external data gaps are limiting Client's ability to serve specific populations (i.e.
thin file, new-to-Client, underserved segments) and model the incremental approval and loss impact of closing those gaps.
Responsibilities
- Evaluation design and execution. Design and run rigorous analyses (including coverage rates, incremental lift, etc.) for new external data sources across bureau, open banking, and alternative data categories. Define the right population, baseline, methodology, and success criteria for each evaluation in partnership with the strategy team before analysis begins.
- Incremental value measurement. Measure the marginal contribution of each data source over Client's existing data signals. Champion-challenger testing, population swap analysis, and coverage gap quantification are core tools.
- Retrospective analytics. Build and maintain the analytical framework that tracks post-integration performance for every data source in the portfolio. At six and twelve month