- 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 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.
- 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 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.
- Must Have
- 3+ years of experience in data science, quantitative analytics, or credit risk modeling in financial services or fintech
- Strong proficiency in Python and SQL to build, run, and document end-to-end analytical workflows independently
- Hands-on experience building and evaluating machine learning models, including model validation and performance benchmarking
- Demonstrated experience measuring incremental lift or marginal contribution in a credit or risk marketing context
- Familiarity with credit risk concepts such as bureau data, score distributions, population segmentation, approval rate and loss rate tradeoffs
- Strong verbal and written communication skills to clearly communicate analytical results to a non-technical audience
- Comfort operating with ambiguity where many of the data sources you will evaluate will be new to client
- Nice to Have
- Experience evaluating or working with credit bureau data (Transunion, Equifax, Experian, CRIF, Schufa, etc.)
- Experience evaluating or working with open banking data (Plaid, Tink, Finicity, etc.)
- Exposure to international credit markets across UK, Germany, Spain, France, and other EU countries
- Familiarity with vendor economics or unit cost analysis
- Experience working with analytical sandbox environments
- Graduate degree in statistics, economics, computer science, or a related quantitative field
Skills Required
- Familiarity with credit risk frameworks
- Analytical ability to define and set thresholds for key metrics
- BI / Data engineering to assist with new ETL jobs
- BI reporting experience for building dashboards to track metrics
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
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