Senior Data Scientist (Credit Risk)
New
United StatesFull-TimeSenior
Salary150,000 - 185,000 USD per year
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Job Details
- Experience
- Minimum of 3 years
- Required Skills
- PythonSQLTableauData visualization
Requirements
- 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.
- Excellent documentation skills and experience in preparing audit-ready deliverables.
- Master’s degree in Economics, Statistics, Mathematics, Data Science or a related quantitative discipline.
Responsibilities
- 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.
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