Risk Data Analyst Jobs in California
Risk Data Analyst jobs in California are among the most active in the country, concentrated in financial services, insurance, technology, and healthcare across a wide range from entry-level analyst roles through senior and principal positions. The largest hiring volumes sit in San Francisco, Los Angeles, and San Diego, where employers such as Wells Fargo, Pacific Life, and Kaiser Permanente maintain substantial analytics teams. The most in-demand specialties in California include credit risk modeling, regulatory reporting, and quantitative risk analytics. Find a role that fits below and apply directly.
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Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
Staff Data Scientist – Fraud & Risk
Job Overview
We are seeking a skilled and motivated Staff Data Scientist to join our Fraud & Risk Data Science team. As an advanced-level individual contributor, you will design, build, and optimize advanced DS/ML models that power our core fraud detection and risk management solutions. You will lead technical initiatives, mentor peers, and drive functional productivity and project success. You will work hands-on with advanced deep learning models, driving delivery of impactful solutions for fraud detection, risk management, and identity verification. This role requires deep technical expertise, strategic ownership, and a commitment to Socure’s leadership principles, including continuous learning, effective communication, and accountability.
Job Responsibilities
Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges.
Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images.
Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments.
Take ownership of project outcomes, data quality, and delivery timelines; proactively escalate issues and work collaboratively to resolve challenges.
Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning.
Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions.
Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection.
Present findings and recommendations to technical and executive stakeholders with clarity and influence.
Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems.
Model Socure’s embedded leadership competencies: continuous learning, effective communication, accountability, team development, decision making, and managing change.
Job Requirements
Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field; or equivalent professional experience.
8+ years of experience in data science, machine learning, or related fields, ideally in a high-growth tech or fintech environment.
Experience in fraud prevention, risk modeling, or identity verification.
Years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs/RNNs, and graph learning).
Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images.
Strong proficiency in Python, SQL, and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development.
Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus)
Experience with LLMs and Agentic AI framework/infrastructure (e.g., LangChain/LangGraph/Ray) is a plus.
Demonstrated ability to proactively deliver complex outcomes, mentor others, and influence cross-functional decisions.
Excellent communication skills with the ability to translate complex data problems into actionable business insights for both technical and non-technical audiences.
Commitment to continuous learning, professional integrity, and high standards of business ethics.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
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Compensation Range: $191K - $230K
See All 13 Risk Data Analyst Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Risk Data Analyst JobsRisk Data Analyst Jobs by City in California
Where California roles are concentrated, by current openings.
Risk Data Analyst Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring



Top Industries Hiring
- Retail
- Human Resources
- Technology & Software
What California Employers Look For
The qualifications that appear most often in risk data analyst jobs across California.
- Bachelor's or master's degree in statistics, mathematics, finance, or a related quantitative field
- Proficiency in SQL, Python, or R for data extraction, modeling, and analysis
- Experience with risk frameworks such as Basel III, CCAR, or enterprise risk management
- Familiarity with California-regulated financial or insurance reporting requirements and compliance standards
- Ability to build and validate predictive models and communicate findings to non-technical stakeholders
- Experience with data visualization tools such as Tableau, Power BI, or similar platforms
Risk Data Analyst Jobs in California: Frequently Asked Questions
How do you become a risk data analyst in California?
Most risk data analysts in California enter the field with a bachelor's degree in statistics, mathematics, economics, or a related quantitative discipline, though many employers prefer or require a master's degree for senior roles. California does not require a state-issued license specific to this role. Building proficiency in SQL, Python, and statistical modeling, combined with coursework or certifications in risk management such as the Financial Risk Manager designation, strengthens candidacy significantly in California's competitive financial and technology markets.
How much do risk data analysts make in California?
Risk data analysts in California earn a median of about $129,110 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $76,610 for the lowest 10% to over $208,360 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire risk data analysts in California?
Employers hiring risk data analysts in California right now include Kaiser Permanente, Block, and Lyft, based on current listings on Migrate Mate as of September 2026. California's concentration of large banks, insurance carriers, and technology firms headquartered in the state creates consistent and varied demand for this role year-round.
Which California cities have the most risk data analyst jobs?
San Francisco, Oakland, and San Francisco Bay Area have the most risk data analyst openings in California. San Francisco's density of banks, fintech companies, and investment firms drives the highest concentration, while Los Angeles draws openings from insurance carriers, entertainment finance, and healthcare systems, and San Diego's growth in biotech and financial services accounts for activity in that metro.
Are there remote risk data analyst jobs in California?
Yes, and more than most fields. Risk data analyst work is predominantly desk-based and data-driven, making it well suited to remote or hybrid arrangements. About 100% of risk data analyst openings tied to California are remote or hybrid as of September 2026, reflecting how broadly employers have adopted flexible models for analytical roles. Model validation, regulatory reporting, and data pipeline work are the functions most commonly performed fully remotely.
How can I get hired as a risk data analyst in California with little or no experience?
The most realistic entry path is through a junior analyst or data analyst associate role at a California bank, insurer, or healthcare organization, where structured rotational programs at employers such as Wells Fargo, Anthem, and Pacific Life often accept recent graduates. Candidates moving laterally from adjacent roles in financial analysis, actuarial support, or data engineering find the transition straightforward. A portfolio of quantitative projects, a Financial Risk Manager or Chartered Financial Analyst candidacy, and demonstrated SQL and Python skills give California applicants a concrete edge over other entry-level candidates.
Where can I find and apply to risk data analyst jobs in California?
You can find and apply to risk data analyst jobs in California on Migrate Mate, which lists current California openings from employers across the state. Find roles that fit your experience and location, then apply directly to each one through the listing.
See All 13 Risk Data Analyst Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
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