Building, maintaining and enhancing credit risk models for lending portfolios.
Extract, clean and manipulate large data sets using SQL and Python; build pipelines and analytics to perform model and portfolio monitoring.
Perform exploratory data analysis (EDA) to identify portfolio trends, drivers of loss performance (vintage, credit bands, borrower attributes, macro factors) and provide insight into model deviations.
Maintain forecast deliverables: monthly/quarterly loss forecasts by vintage and segment, stress and scenario analyses, sensitivity testing.
Provide commentary and insights to business stakeholders on credit policy assumptions, model health, and emerging portfolio risks.
Automate reporting, dashboards and pipelines to streamline model monitoring and improve efficiency and accuracy.
Document model methodologies, assumptions, data sources and results in clear, audit-ready format consistent with risk governance requirements.
Participate in governance and review of credit model methodology, model validation support and liaise with external auditors or regulators where needed.
Continuously identify opportunities to improve credit decisioning accuracy, data infrastructure, modeling techniques, and integrate advanced statistical or machine-learning techniques as appropriate.

Minimum of 3 years’ hands-on experience in credit risk modeling and portfolio monitoring.
Strong programming skills in Python/SQL for data analysis, modeling and automation.
Solid background in Probability & Statistics
Experience with pricing and price optimization along with analytics and monitoring related to pricing
Experience with credit risk modeling methodologies: Scorecard models, XGBoost, time-series analysis, vintage modeling, roll-rate curves, survival analysis or logistic regression in consumer credit risk context.
Familiarity with data visualization tools (e.g., Tableau, Python Widgets) or dashboarding
Strong analytical and critical thinking skills; ability to interpret results, identify trends, draw actionable insights and communicate clearly to non-technical stakeholders.
Excellent documentation skills and experience in preparing audit-ready deliverables (methodologies, assumptions, model validation support).
Master’s degree in Economics, Statistics, Mathematics, Data Science or a related quantitative discipline (PhD preferred, but not required).

401 (k) with employer match
Medical, dental, and vision with HSA and FSA options
Competitive vacation and sick time off, as well as dedicated volunteer days
Access to wellness support through Employee Assistance Program, Talkspace, and fitness discounts
Up to $5,250 paid back to you on eligible education expenses
Pet care discounts for your furry family members
Financial support in times of hardship with our Achieve Care Fund
A safe place to connect and a commitment to diversity and inclusion through our six employee resource groups

Senior Data Scientist – Credit Risk

Key skills