Divine Research Inc.
Data Scientist - Credit & Risk
- Compensation
- Not disclosed
- Location
- San Francisco, California
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Data science
- Posted
Skills
Must have
- 4+ years of experience in decision science, credit risk analytics, or a related quantitative role in fintech or consumer lending.
- Deep proficiency in Python and SQL, with end-to-end analytical ownership.
- Strong understanding of PD/LGD modeling, scorecard development, reject inference, vintage analysis, and risk segmentation.
- Experience monitoring credit risk metrics and portfolio performance, including loss forecasting and underwriting model improvement.
- Ability to influence cross-functional teams and senior stakeholders and communicate analytical findings clearly.
- Experience designing and evaluating experiments in a consumer product context.
- Comfort with ambiguity, minimal oversight, problem-solving, and attention to detail.
- Experience building or maintaining large-scale data pipelines for B2C financial products.
- Familiarity with credit bureau data, cash flow underwriting, or alternative data sources.
- Experience working in emerging markets and consumer financial products.
- Strong understanding of DeFi protocol mechanics and onchain data tooling.
- Exposure to consumer-credit regulatory frameworks such as FCRA or ECOA.
Full description
Own portfolio monitoring and reporting for an unsecured stablecoin lending system, research emerging risk trends, and turn borrower behavioral data into actionable recommendations for credit strategy and underwriting policy. The role may expand into broader product analytics.
Responsibilities
- Monitor credit risk models, including underwriting, loss forecasting, and fraud detection, and iterate based on portfolio performance.
- Design and maintain scalable data pipelines, monitoring infrastructure, and dashboards for portfolio health, user behavior, and risk indicators.
- Partner with product, research, and engineering teams to define metrics and develop credit and growth strategies.
- Design and analyze A/B tests, quasi-experiments, and causal inference studies for product and policy changes.
- Produce portfolio monitoring and investigative analyses with recommendations.
- Translate quantitative findings into clear narratives for product, leadership, and cross-functional stakeholders.
Requirements
- 4+ years of experience in decision science, credit risk analytics, or a related quantitative role in fintech or consumer lending.
- Deep proficiency in Python and SQL, with end-to-end analytical ownership.
- Strong understanding of PD/LGD modeling, scorecard development, reject inference, vintage analysis, and risk segmentation.
- Experience monitoring credit risk metrics and portfolio performance, including loss forecasting and underwriting model improvement.
- Ability to influence cross-functional teams and senior stakeholders and communicate analytical findings clearly.
- Experience designing and evaluating experiments in a consumer product context.
- Comfort with ambiguity, minimal oversight, problem-solving, and attention to detail.
- Experience building or maintaining large-scale data pipelines for B2C financial products.
- Familiarity with credit bureau data, cash flow underwriting, or alternative data sources.
- Experience working in emerging markets and consumer financial products.
- Strong understanding of DeFi protocol mechanics and onchain data tooling.
- Exposure to consumer-credit regulatory frameworks such as FCRA or ECOA.