Senior Manager Credit Risk Strategy

EXL Talent Acquisition Team; Gurugram District; Vor Ort; INR 1.200.000 - 1.800.000; Vollzeit

Zusammenfassung

EXL Talent Acquisition Team in India seeks a Senior Data Analytics professional focused on risk analytics. You will design and execute evaluations of external data sources, defining populations, baselines, and success criteria in collaboration with strategy.

Responsibilities include measuring incremental value, conducting champion-challenger tests, retrospective analytics, and gap analysis to improve credit risk outcomes across client portfolios.

Qualifikationen

• 3+ years in data science or credit risk analytics 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 data sources you will evaluate will be new to client
• Nice to Have: experience with credit bureau data, open banking data, exposure to international credit markets, vendor economics, sandbox environments

Aufgaben

• Evaluation design and execution for new external data sources across bureau, open banking, and alternative data categories
• Incremental value measurement of data sources over client signals; champion-challenger testing and population swap analysis
• Retrospective analytics to track post-integration performance and verify lift projections over 6 and 12 months
• Coverage and population analysis to identify data gaps and model incremental impact of closing those gaps

Kenntnisse

Python

SQL

ML models

Data storytelling

Communication

Jobbeschreibung

• Job Role Business Analytics-Risk Analytics
• Experience (In Years) 6-9

Job Description

• 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.

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 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.

Qualifications

• 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