Data Scientist - Credit Risk

Join Divine as a Data Scientist to drive credit risk intelligence for their undercollateralized lending system.

Work remotely with a competitive salary.

Traditional credit was built for people who already have money.

Requirements for credit history, collateral, and costly underwriting create insurmountable barriers for those who need capital most.

Over 1.4 billion people lack access to credit.

A vendor in Lagos earns cash daily but can't prove a steady income.

A Colombian nurse with years of perfect informal repayments remains invisible to banks.

We built an alternative called Credit.

Since December 2024, it has issued over one million undercollateralized loans using stablecoins.

People from around the world have used these loans to pay for things like groceries, medicine, and transportation.

Backed by $6.6 million from Paradigm and Nascent, we're scaling a system that has already reached more than 900,000 unique borrowers.

Help us take it to the next level.

## What you'll do

• •Monitor credit risk models, including underwriting, loss forecasting, and fraud detection, and iterate based on observed portfolio performance.
• •Design, build, and maintain scalable data pipelines, monitoring infrastructure, and dashboards to track portfolio health, user behavior, and key risk indicators.
• •Partner with product, research, and engineering teams to define north star metrics and translate them into measurable, actionable credit and growth strategies.
• •Design and analyze A/B tests, quasi-experiments, and causal inference studies to evaluate the impact of product and policy changes.
• •Produce portfolio monitoring and investigative analyses, making recommendations based on findings.
• •Translate complex quantitative findings into clear, compelling narratives for product, leadership, and cross-functional stakeholders.

• •4+ years of experience in decision science, credit risk analytics, or a closely related quantitative role within fintech or consumer lending.
• •Deep proficiency in Python and SQL; comfortable owning analyses end-to-end from raw data to recommendation.
• •Strong understanding of credit risk modeling concepts, including PD/LGD modeling, scorecard development, reject inference, vintage analysis, and risk segmentation.
• •Demonstrated experience monitoring credit risk metrics and portfolio performance, including loss forecasting and underwriting model improvement.
• •Proven ability to influence and collaborate with cross-functional teams and senior stakeholders, with a track record of translating analytical findings into accessible, actionable insights.
• •Experience designing and evaluating experiments (A/B tests, holdout groups, or causal inference frameworks) in a consumer product context.
• •Comfortable with ambiguity and biased toward action; thrives with minimal oversight and brings strong problem-solving skills and sharp attention to detail.