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Credit Risk Data Science Jobs in California (NOW HIRING)

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Director, VCA NA Risk Practice

Foster City, CA · On-site

This role requires deep expertise in credit risk frameworks, and data analytics, with a strong ... Partner closely with data science teams to deploy and interpret predictive models, model forecasts ...

Chase

Environmental Credit Risk Sr Officer

Irvine, CA · On-site

Environmental Credit Risk Associate Bring your expertise to JPMorganChase. As part of Risk ... Bachelor's degree or equivalent education in environmental sciences or related fields, and may hold ...

Sunbit

Analytics Manager, Credit Cards

Los Angeles, CA · On-site +1

Requirements What you'll bring: * 6+ years of experience in Analytics, Data Science, Decision Science, Credit Risk, or a related quantitative function. * 4+ years supporting Credit Card portfolios ...

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Director, VCA NA Risk Practice

San Francisco, CA · On-site

This role requires deep expertise in credit risk frameworks, and data analytics, with a strong ... Partner closely with data science teams to deploy and interpret predictive models, model forecasts ...

Visa

Director, VCA NA Risk Practice

San Francisco, CA · On-site

This role requires deep expertise in credit risk frameworks, and data analytics, with a strong ... Partner closely with data science teams to deploy and interpret predictive models, model forecasts ...

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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 July 2026, with employment types broken down into 82% Full Time, 16% Part Time, 1% Temporary, and 1% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.
Fraud & Risk Data Analyst Fintech & Data Insights *** Direct End Client ***

Fraud & Risk Data Analyst Fintech & Data Insights *** Direct End Client ***

Projas Technologies, LLC

Mountain View, CA • On-site

Other

Posted 12 days ago

Job description

Staff Fraud & Risk Analyst Fintech & Data Insights

As a Fraud & Risk Data Analyst, you ll work at the intersection of analytics, compliance, and product innovation. This role is part of a dynamic Risk Insights and Experimentation team that influences billions of dollars in transactions annually. You ll partner with cross-functional teams to uncover insights, design experiments, and shape strategies that drive secure, scalable growth.

Domain expertise in Security, Risk & Fraud in Fintech space (consumer Lending, Fraud & Risk Policy, Compliance, Marketing, Product, Finance is required)

Key Responsibilities:
  • Deliver clear, persuasive insights to senior leadership through data storytelling and executive-ready reports.
  • Build intuitive dashboards and visualizations that guide strategic and operational decisions.
  • Design and interpret A/B tests to optimize product performance and risk strategies.
  • Collaborate with product, compliance, finance, and data science teams to define scalable analytics frameworks.
  • Analyze large-scale transactional and behavioral data to detect trends, anomalies, and fraud patterns.
  • Evaluate compliance effectiveness and recommend improvements to onboarding and remediation flows.
  • Translate ambiguous business questions into structured analytical approaches using SQL, Python, and visualization tools.
  • Investigate root causes of performance gaps and propose timely corrective actions.
  • Maintain clean, reliable data pipelines and address data quality issues proactively.
Qualifications:
  • 6+ years in analytics, data science, or decision strategy roles, ideally in Financial Services or Fintech.
  • Advanced SQL skills and working knowledge of Python for data manipulation and automation.
  • Experience with data visualization tools (Tableau, Quicksight, Qlik) and dashboard development.
  • Strong understanding of A/B testing principles and experimental design.
  • Proven ability to influence decisions through data-driven recommendations.
  • Excellent communication skills for translating complex data into actionable insights.
  • Bachelor s degree in a quantitative field (Economics, Math, Science) or equivalent experience.

fraud analyst, risk analyst, fintech analytics, SQL, Python, A/B testing, data visualization, Tableau, Quicksight, Qlik, compliance analytics, risk management, dashboard development, data science, financial services, experimentation, policy analytics, fraud detection, risk strategy, KPIs, OKRs, data storytelling

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