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Credit Risk Data Science Jobs in California (NOW HIRING)

SoFi

Credit Strategy - Risk Lead

San Francisco, CA · On-site

... data science and analytics. The team designs data-driven strategies to ensure the growth in lending is consistent with the company's risk appetite and helps create the products and experiences that ...

Rich Data Co

Senior Data Scientist

San Jose, CA

Bachelor's degree in in Statistics, Mathematics, Physics, Computer Science, or other Quantitative related degree. * 5+ years of data science experience, ideally in credit risk or financial services.

Rich Data Co

Senior Data Scientist

San Jose, CA · On-site

$150K - $175K/yr

Bachelor's degree in in Statistics, Mathematics, Physics, Computer Science, or other Quantitative related degree. * 5+ years of data science experience, ideally in credit risk or financial services.

SoFi

Senior Credit Manager

San Francisco, CA

... and risk and operational data science and analytics. The team designs data-driven strategies to ... The Senior Credit Manager will work in the Credit team and have responsibilities to analyze and ...

SoFi

Credit Strategy - Risk Lead

San Francisco, CA · On-site

... data science and analytics. The team designs data-driven strategies to ensure the growth in lending is consistent with the company's risk appetite and helps create the products and experiences that ...

Rich Data Co

Senior Data Scientist

San Jose, CA · On-site

... of data science experience,ideally in credit riskor financial services. * Experience developing and managing quantitative credit risk models including credit decisioning models. * Programming ...

SoFi

Senior Credit Manager

San Francisco, CA · On-site

What you'll need: * 7+ years of unsecured credit risk and data science experience * Business acumen and work experience in the consumer lending business (loans or credit cards) * Direct experience in ...

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Credit Risk Data Science information

How does a Credit Risk Data Scientist typically collaborate with other teams within a financial institution?

Credit Risk Data Scientists often work closely with credit analysts, risk managers, and IT professionals to develop, validate, and implement models that assess borrower risk. They frequently participate in cross-functional meetings to translate complex analytical findings into actionable business insights. Collaboration with compliance and regulatory teams is also common to ensure that risk models meet current regulatory standards. Effective communication and teamwork are essential, as the role bridges technical model development and practical risk management decisions.

What is Credit Risk Data Science?

Credit Risk Data Science is a specialized field that uses statistical analysis, machine learning, and data modeling techniques to assess and predict the likelihood that a borrower will default on a loan or credit obligation. Professionals in this field analyze large datasets from financial transactions, credit reports, and market trends to develop models that help financial institutions make informed lending decisions. Their work helps manage risk, set appropriate interest rates, and comply with regulatory standards. By leveraging advanced analytics, credit risk data scientists play a crucial role in minimizing losses and maximizing profitability for banks and lenders.

What are the key skills and qualifications needed to thrive as a Credit Risk Data Scientist, and why are they important?

To thrive as a Credit Risk Data Scientist, you need strong analytical skills, proficiency in statistical modeling, and a solid background in finance, mathematics, or a related field, often supported by an advanced degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of credit risk modeling tools such as SAS or SQL are typically required. Critical thinking, attention to detail, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These abilities are crucial for building accurate risk models, informing strategic decisions, and ensuring regulatory compliance in financial institutions.
What are popular job titles related to Credit Risk Data Science jobs in California? For Credit Risk Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Credit Risk Data Science jobs in California look for? The top searched job categories for Credit Risk Data Science jobs in California are:
What cities in California are hiring for Credit Risk Data Science jobs? Cities in California with the most Credit Risk Data Science job openings:
Credit Risk Data Science jobs near you
Infographic showing various Credit Risk Data Science job openings in California as of June 2026, with employment types broken down into 25% Full Time, 25% Part Time, 25% Temporary, and 25% Contract. Highlights an 50% In-person, and 50% Hybrid job distribution.

Data Scientist - Credit & Risk

Divine Research Inc

San Francisco, CA • On-site

Full-time

Posted 11 days ago

Job description

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. Most lending systems spend a lot to guess who will repay, and yet so many who are creditworthy still can't get a loan.
We built an alternative called Credit. Since December 2024, it has issued over one million unsecured 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.
About the role
We're looking for a data scientist to drive credit risk intelligence across Credit, our leading unsecured lending system. You'll own portfolio monitoring and reporting, research emerging risk trends, and transform borrower behavioral data into actionable guidance that shapes our credit strategy and roadmap.
While our engineering & research teams owns the underlying models, you'll be the person who makes sense of what they're telling us, tracking portfolio health, identifying issues early, and turning insights into clear recommendations for risk strategy and underwriting policy. Over time, this role may expand to drive broader product analytics across our suite of products.
This role is based in San Francisco, California. We work in a hybrid model, with the team in office 3 days per week.
Stack
  • Python
  • SQL
  • Grafana/Prometheus/Metabase
  • Blockchain data and indexing tools (Dune, Shovel)
Key responsibilities
  • 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
Requirements
  • 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
Nice to have
  • Experience building or maintaining large-scale data pipelines supporting B2C financial products
  • Familiarity with credit bureau data, cash flow underwriting, or alternative data sources in credit model development
  • Experience working in emerging markets, ideally on financial products serving everyday consumer needs (microfinance, BNPL, digital lending)
  • Strong understanding of DeFi protocol mechanics (lending, yield vaults, ERC4626) and experience with onchain data tooling (Dune, Shovel, Ponder, Goldsky or similar)
  • Exposure to regulatory frameworks relevant to consumer credit (FCRA, ECOA, or equivalent)

Divine Research is an equal opportunity employer.

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