Support design and monitoring of EWA underwriting strategies and forecasting tools.
Translate analytical findings into clear recommendations for credit policies.
Support champion/challenger tests and experiment design for EWA risk strategies.
Assist in designing exposure limits, eligibility rules, and verification checks.
Query, clean, and analyze large bank transaction datasets using SQL and Python, or R/SAS).
Build dashboards, reports and durable data assets to track customer behavior and credit performance.
Perform EDA, feature engineering, and data quality checks to drive trusted insights.
Prepare model inputs, evaluate new data sources (including open-banking data), and track model performance.
Analyze performance of underwriting strategies and identify opportunities to improve accuracy and reduce losses.
Learn and introduce new analytical tools and techniques relevant to credit risk.
Partner with Product, Engineering, Data Science, and Operations to implement updates to policies and decisioning logic.
Support the coordination of initiatives with data suppliers.
Package insights into crisp narratives and presentations for stakeholders.

Requirements

Degree in Engineering, Computer Science, Statistics, Economics, Finance, or related; or equivalent experience. Advanced degree is a plus.
5+ years of analytics/data science experience.
2+ years of experience in consumer lending, fintech, or banking credit risk (subprime experience is a plus).
Exposure to earned wage access, overdraft-alternatives, or cash-flow-based lending products is a strong plus.
Experience working with bank transaction data and other very large financial datasets.
Proficiency in SQL and at least one analytical language (Python preferred; R or SAS acceptable).
Experience with visualization tools such as Tableau (preferred), Power BI, Looker; strong skills with Excel/Google spreadsheets.
Exposure to experimentation design and tracking.
Experience cleaning, joining, and analyzing large datasets in a cloud data warehouse environment (i.e. Snowflake, BigQuery, Redshift).
Familiarity with open-banking data supplier integrations is desirable (i.e. Plaid, MX, Finicity).

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

SQLPythonRSASdata analysisfeature engineeringdata quality checksEDAcredit risk analysismodel performance evaluation

communicationcollaborationanalytical thinkingproblem-solvingpresentation skills