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Credit Risk Data Science Jobs in California (NOW HIRING)

SoFi

Staff Data Scientist

San Francisco, CA · On-site

... credit risk assessment-ensuring our high-value secured loan products are backed by world-class data science. What You'll Do * Lead the design and implementation of ML models for mortgage-specific use ...

SoFi

Credit Strategy - Risk Lead

San Francisco, CA

... 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

$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.

Inizio Partners

Credit Risk Modeler

San Jose, CA · Hybrid

Master's degree in economics, mathematics, computer science/engineering, operations research or ... Proficient in executive level communications creating slides, decks and building data dashboards

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

... 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

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

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.

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 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
Senior Credit Risk Analyst

Senior Credit Risk Analyst

Artemis Consultants

San Jose, CA • On-site

Full-time

Posted 17 days ago

Job description

COMPANY OVERVIEW:
The company a global leader in enterprise-grade data analytics and AI solutions, committed to empowering businesses across various industries with cutting-edge technology and expert insights. Backed by a top private equity firm, they drive innovation through significant investments and an entrepreneurial spirit.
They focus on delivering advanced Data Analytics & AI Solutions. By combining sophisticated technology with subject matter expertise, they deliver material impact on clients' topline and streamline their operations. They specialize in providing tailored solutions across financial services, CPG, legal, pharma, life sciences, retail and logistics, helping them build robust data analytics and AI capabilities.
With a client base spanning 30 countries, they have a global presence to enables them to offer localized expertise with a worldwide perspective.
POSITION OVERVIEW:
We are seeking a Senior Credit Risk Analyst with strong unsecured lending experience to support client's consumer credit products. This role will focus on credit card and personal loan portfolios, leveraging data to drive risk strategies, optimize underwriting, and improve portfolio performance.
You will work closely with cross-functional partners in Risk, Product, Data Science, and Engineering to design, implement, and monitor credit risk strategies that balance growth, risk, and customer experience.
RESPONSIBILITIES:
  • Develop, implement, and monitor credit risk strategies for unsecured lending products (with a strong focus on credit cards).
  • Analyze portfolio performance, customer behavior, and risk trends to identify opportunities to improve approval rates, loss performance, and profitability.
  • Design and execute A/B tests and experiments to evaluate new risk policies, cutoffs, and treatment strategies.
  • Build and maintain dashboards, reports, and performance tracking for key risk metrics (e.g., delinquency, losses, vintage curves, line utilization, approval/decline rates).
  • Partner with data science and modeling teams to translate model outputs into actionable strategies and policy rules.
  • Use SQL and Python to extract, clean, and analyze large datasets from multiple sources.
  • Present insights, recommendations, and business cases to senior stakeholders in a clear, structured manner.
  • Support regulatory and compliance requirements by ensuring risk strategies and analyses meet internal and external standards.
  • Contribute to continuous improvement of data, tools, and processes within the Credit Risk and Analytics function.

PREFERRED PROFILE:
  • Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Economics, Engineering, Computer Science, Finance) or equivalent practical experience.
  • 5+ years of hands-on experience in Credit Risk within unsecured lending: ideally in: Credit cards (strongly preferred), and/or Personal loans or other unsecured consumer lending products.
  • Strong SQL skills with demonstrated experience querying and manipulating large, complex datasets.
  • Proficiency in Python for data analysis, modeling support, and automation (e.g., pandas, NumPy, basic visualization libraries).
  • Proven track record of using analytics to solve business problems in credit risk (e.g., underwriting, line management, pricing, collections, fraud/risk trade-offs).
  • Solid understanding of core credit risk concepts: scorecards, cutoffs, PD/LGD/EAD, vintage analysis, loss curves, risk-based pricing, and portfolio segmentation.
  • Strong problem-solving skills with the ability to structure ambiguous problems, form hypotheses, and drive analyses end-to-end.
  • Excellent communication skills, with the ability to translate complex analytical findings into clear, actionable recommendations for non-technical stakeholders.
  • Experience working in a fintech, payments, or digital lending environment.
  • Familiarity with credit bureau data and alternative data sources.
  • Experience with experimentation (A/B testing), champion-challenger frameworks, and test design.
  • Exposure to machine learning-driven risk models and their application in production environments.
  • Experience with BI/visualization tools (e.g., Tableau, Power BI, Looker) for dashboarding and reporting.
  • Master's degree in a quantitative discipline is a plus.

Personal Attributes Desired:
  • Data-driven, detail-oriented, and intellectually curious.
  • Comfortable working in a fast-paced, evolving environment.
  • Collaborative mindset with the ability to work effectively across functions and geographies.
  • Ownership mentality and bias for action; able to drive initiatives from concept to execution.

LOCATION: San Jose, CA (Hybrid)
Job ID# 3599749
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