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Fintech Data Science Jobs in California (NOW HIRING)

Brigit

Director of Data Science

San Francisco, CA · On-site

$225K - $250K/yr

Forbes Fintech 50 2022 Role Overview: At Brigit, our Data Science team has been dramatically scaling its impact. We're aiming to integrate new models across several product domains over the next year ...

3B Staffing LLC

Data Scientist

Oakland, CA · Remote

$58/hr

Degree in Mathematics, Statistics, Computer Science, or related field * 2+ years of industrial ... Experience in credit risk / lending or fintech domain * Deep understanding of credit risk modeling ...

Earnest

Data Scientist

San Francisco, CA · On-site +1

$194K/yr

Master's degree in Data Science, Statistics, Business Analytics, or a related field, plus 4 years ... Fintech sectors to drive informed decision-making and optimize business processes. This notice is ...

Earnest Co

Data Scientist

San Francisco, CA · On-site +1

$194K/yr

Master's degree in Data Science, Statistics, Business Analytics, or a related field, plus 4 years ... Fintech sectors to drive informed decision-making and optimize business processes. This notice is ...

Block

Data Scientist

Bodega Bay, CA

Reporting to the Sales Data Science lead, you'll own a mature subdomain of our Sales function and ... Familiarity with fintech, payments, or the SMB merchant space We value strong fundamentals and ...

Walmart

Principal, Data Scientist

Hayward, CA · On-site

$143K - $286K/yr

This role is centered on building scalable, end-to-end Data Science and AI solutions that improve ... consumer technology, fintech, or other data-intensive industries. * Strong communication ...

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Fintech Data Science information

See California salary details

$37K

$121.1K

$193.9K

How much do fintech data science jobs pay per year?

As of Jul 26, 2026, the average yearly pay for fintech data science in California is $121,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,200.00 and $134,200.00 per year, depending on experience, location, and employer.

What is a Fintech Data Scientist?

A Fintech Data Scientist is a professional who uses data analysis, machine learning, and statistical techniques to solve problems and create value within the financial technology (fintech) industry. They work with large amounts of financial data to develop predictive models, detect fraud, assess risk, and optimize financial products or services. Their expertise combines knowledge of finance, programming, and advanced analytics to help fintech companies make data-driven decisions and innovate in areas such as payments, lending, and investment. Fintech Data Scientists often collaborate with engineers, product managers, and business stakeholders to deliver actionable insights that drive business growth.

How do data scientists in fintech typically collaborate with product and engineering teams to develop new financial products?

In fintech, data scientists often work closely with product managers and engineering teams throughout the lifecycle of a financial product. They analyze user data and market trends to provide actionable insights during the product design phase, and collaborate with engineers to implement machine learning models into the product infrastructure. Regular cross-functional meetings and agile workflows are common, allowing data scientists to iterate on models based on feedback and evolving requirements. This collaborative environment ensures that data-driven solutions are robust, scalable, and aligned with business goals.

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

To thrive as a Fintech Data Scientist, you need a strong background in statistics, machine learning, and programming (often with a degree in computer science, mathematics, or a related field). Familiarity with tools such as Python, R, SQL, cloud computing platforms, and experience with financial data modeling or relevant certifications are typically required. Strong problem-solving skills, attention to detail, and effective communication are important soft skills that set top professionals apart. These skills and qualities are vital for extracting actionable insights from complex financial data, driving innovation, and ensuring regulatory compliance in the fast-evolving fintech industry.

What is the difference between Fintech Data Science vs Fintech Data Analyst?

AspectFintech Data ScienceFintech Data Analyst
Required SkillsAdvanced statistical, programming, and machine learning skillsData interpretation, reporting, and basic analytics
CertificationsData Science certifications, programming coursesData analysis or business intelligence certifications
Work EnvironmentDeveloping models, algorithms, and predictive analyticsData reporting, dashboards, and data cleaning
Industry UsageCreating predictive models for risk, fraud detection, and customer insightsGenerating reports, supporting decision-making with data

Fintech Data Science involves building complex models and applying machine learning techniques, requiring advanced skills and certifications. Fintech Data Analysts focus on interpreting data, creating reports, and supporting business decisions with less technical complexity. Both roles are essential in the fintech industry but differ in technical depth and responsibilities.

What are popular job titles related to Fintech Data Science jobs in California? For Fintech Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Fintech Data Science jobs in California look for? The top searched job categories for Fintech Data Science jobs in California are:
What cities in California are hiring for Fintech Data Science jobs? Cities in California with the most Fintech Data Science job openings:
Fintech Data Science jobs near you
Infographic showing various Fintech Data Science job openings in California as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $121,131 per year, or $58.2 per hour.
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 27 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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