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Fintech Data Science Jobs in California (NOW HIRING)

Taskrabbit

Staff Data Scientist

San Francisco, CA · On-site

$170K - $225K/yr

A minimum of 7 years industry experience in data science; previous experience in a marketplace or fintech company is a plus * Strong and relevant experience with advanced experimentation and ...

Taskrabbit

Staff Data Scientist

San Francisco, CA

$170K - $225K/yr

A minimum of 7 years industry experience in data science; previous experience in a marketplace or fintech company is a plus * Strong and relevant experience with advanced experimentation and ...

Mudflap

Senior Data Scientist, Risk

Palo Alto, CA · On-site

$220K - $245K/yr

... fintech platform that processes billions in annual payment volume. We're building a data-driven ... Drive strategic data science initiatives that directly impact business outcomes, working closely ...

1 point system

Data Analyst

Sunnyvale, CA · On-site

Master's degree in data science, Statistics, Computer Science, Economics, or a related field. * 4+ years of industry experience in Payments, Risk, FinTech, or Marketplace Analytics. * Proven ability ...

Intuit

Senior Staff Data Scientist, Money

Oakland, CA · On-site

Qualifications We're looking for a curious, proactive, and influential data science leader with a passion for fintech. * 9+ years of experience in data science and analytics, with a track record of ...

Intuit

Senior Staff Data Scientist, Money

Oakland, CA · On-site

Qualifications We're looking for a curious, proactive, and influential data science leader with a passion for fintech. * 9+ years of experience in data science and analytics, with a track record of ...

Intuit

Senior Staff Data Scientist, Money

Oakland, CA · On-site

Qualifications We're looking for a curious, proactive, and influential data science leader with a passion for fintech. * 9+ years of experience in data science and analytics, with a track record of ...

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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 Sep 12, 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.

What are the key skills and qualifications needed to thrive as a fintech data scientist?

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.

How do fintech data scientists 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 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.

Is data science good for fintech?

Data science is highly valuable in fintech, as it enables the development of algorithms for risk assessment, fraud detection, and personalized financial services. Fintech companies often rely on data analysis, machine learning, and statistical modeling to improve decision-making and customer experience. Skills in programming, data manipulation, and financial knowledge are essential for data scientists in this field.

Is fintech data science a high paying career?

Fintech data science is generally a high-paying career due to the demand for advanced analytics and machine learning skills in financial technology companies. Salaries often depend on experience, education, and technical expertise in tools like Python, R, and SQL, with senior roles earning significantly more. The field offers competitive compensation compared to many other data science roles across industries.

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 August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $121,131 per year, or $58.2 per hour.

Data Scientist II

Mountain View, CA • On-site

Gravity IT Resources
Recruiting and Staffing Services • 51 - 200 employees
Learn more about Gravity IT Resourcesexternal

Other

Posted 9 days ago

Job description

Data Scientist II
Job Type: Contract, Full-Time (Potential to extend)
Location: San Diego, CA (Onsite)

We’re partnering with a leading fintech company in San Diego to hire a Data Scientist II supporting their Customer Success organization’s operations and training analytics team. This is a contract engagement, working directly with internal and external stakeholders to turn data into insight the business can act on.

This is a hands‑on, business‑facing analytics role rather than a pure modeling position: you’ll partner with stakeholders to figure out what actually needs answering, then use statistical analysis, predictive modeling, and data mining to get there. There’s also room to occasionally guide less experienced data analysts as you take on the work.

What You’ll Do
  • Perform business analysis using statistical analysis, explanatory and predictive modeling, and data mining to develop actionable insights and recommendations for current business operations.
  • Work directly with internal and external stakeholders to identify analytical requirements, translating business questions into structured analysis.
  • Apply causal inference techniques, including propensity score matching, difference-in-differences, and synthetic control methods, to evaluate what’s actually driving outcomes.
  • Use Generative AI and other emerging technologies to accelerate insights from multi‑modal data.
  • Build and maintain reporting and dashboards using advanced SQL and visualization tools such as Qlik, QuickSight, Tableau, or Plotly Dash, including ad hoc data pulls and reports as needed.
  • Help implement or develop systems to capture business operations information, and occasionally guide less experienced data analysts.
Required Qualifications
  • BS or MS degree in Statistics, Mathematics, Computer Science, or a related field.
  • Five (5) or more years of experience in data science or product analytics, with a strong foundation in predictive modeling, customer segmentation, and experimentation.
  • Advanced SQL skills and proficiency with a modern visualization tool (Qlik, QuickSight, Tableau, or Plotly Dash).
  • Strong Python skills for statistical analysis and modeling, including libraries such as NumPy, Pandas, and Scikit-learn.
  • Experience with causal inference techniques, including propensity score matching, difference-in-differences, and synthetic control methods.
  • Experience designing and interpreting complex experiments beyond traditional A/B testing methods.
Preferred Qualifications
  • Fintech or financial services experience.
  • Demonstrated experience building reusable, scalable analytics solutions rather than one‑off analyses.
  • Track record of formulating data‑backed strategies that drive measurable business growth.
What We’re Looking For

This role calls for someone who moves fluidly between the technical and the practical: comfortable in Python and SQL, but just as comfortable in a room with stakeholders figuring out what question actually needs answering. We’re looking for a quick learner who can work independently in a fast‑paced environment, communicate complex findings clearly enough to influence decision‑makers, and build genuinely reusable analytics rather than one‑off reports.

Equal Employment Opportunity Statement

Gravity IT Resources is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected characteristic. All employment decisions are based on qualifications, merit, and business needs.

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