Credit Strategy Data Analyst

Self Financial•Austin, TX

3d•$101,000 - $150,000

About The Position

Self Financial is a venture-backed, high-growth FinTech company with a mission to increase economic inclusion and financial resilience by empowering people to build credit and build savings. We're looking for people who share our passion and are driven to tackle challenges, find solutions and make the financial space better for the communities we serve. Our team is passionate about challenging the status quo of the credit industry by providing people accessible tools to take control of their credit. Executing on our mission requires deep collaboration across our teams to ensure our products reach the people who can benefit from them the most, particularly the 100 million+ Americans who have no or low credit. We celebrate diversity and are committed to creating an inclusive environment for all employees. To that end, we seek to recruit, develop and retain the most talented people from a diverse candidate pool. About the Role We are expanding our earned wage access (EWA) offering and seeking a Credit Strategy Data Analyst with strong analytical skills and experience working with consumer credit, open-banking data, or real-time underwriting strategies. This role focuses on the design and analysis of models, data foundations, reporting, and data-driven strategies to improve credit risk management for the EWA product. You’ll be instrumental in shaping strategies that balance growth and portfolio credit risk.

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).
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).
Strong analytical mindset and attention to data quality.
Ability to translate data findings into clear recommendations.
Curiosity, ownership mindset, and comfort working in a fast-moving environment.
Collaborative approach and willingness to learn new data tools and risk frameworks.

Nice To Haves

Exposure to earned wage access, overdraft-alternatives, or cash-flow-based lending products is a strong plus.
Familiarity with open-banking data supplier integrations is desirable (i.e. Plaid, MX, Finicity).

Responsibilities

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.