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Entry Level Fintech Data Scientist Jobs in California

Preferred : • Experience in consumer tech, gaming, fintech, or marketplace data science, particularly in monetization, LTV modeling, or experimentation. • Prior experience as a quantitative ...

Data Scientist (AHL)

San Mateo, CA · On-site

$140 - $160/hr

Since 2002, Achieve has grown into one of the largest private consumer fintech unicorns in the U.S ... As a Data Scientist (potential uplevel to Sr. Data Scientist for the right experience), you'll ...

Data Scientist (AHL)

San Mateo, CA · On-site

$140K - $160K/yr

Since 2002, Achieve has grown into one of the largest private consumer fintech unicorns in the U.S ... As a Data Scientist (potential uplevel to Sr. Data Scientist for the right experience) , you'll ...

Since 2002, Achieve has grown into one of the largest private consumer fintech unicorns in the U.S ... As a Data Scientist (potential uplevel to Sr. Data Scientist for the right experience) , you'll ...

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 ...

A FinTech Unicorn Powering Financial Progress with AI At Kikoff, our mission is to provide ... As a Data Scientist at Kikoff, you work closely with cross functional teams incl. Product ...

Staff Data Scientist

San Francisco, CA · On-site

$170 - $225/hr

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 ...

Data Scientist, Analytics at Thatch Location Austin, Texas, United States; New York, New York ... Working in healthcare, fintech, or other complex, regulated domain. How we work * We move quickly ...

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Entry Level Fintech Data Scientist information

What does an entry level fintech data scientist do?

An Entry Level Fintech Data Scientist works with financial data to build models, analyze trends, and help companies make data-driven decisions. They typically use programming languages like Python or R, along with statistical and machine learning techniques, to extract insights from large financial datasets. Their day-to-day tasks may include cleaning data, developing predictive models, visualizing results, and collaborating with finance and engineering teams. This role is crucial for improving financial products, detecting fraud, and optimizing business strategies in the fintech industry.

What are the key skills and qualifications needed to thrive as an entry level fintech data scientist, and why are they important?

To thrive as an Entry Level Fintech Data Scientist, you need a solid background in statistics, programming (Python or R), and foundational knowledge of finance or economics, often supported by a relevant degree. Familiarity with data analysis tools such as SQL, machine learning frameworks like scikit-learn, and experience with cloud platforms or financial databases are typically required. Strong problem-solving skills, attention to detail, and effective communication help you interpret data insights and present findings to both technical and non-technical stakeholders. These competencies are crucial for developing accurate data models and delivering actionable insights in the fast-paced fintech industry.

What are some common challenges faced by entry level fintech data scientists, and how can they overcome them?

Entry-level data scientists in fintech often encounter challenges such as working with highly sensitive financial data, navigating complex regulatory requirements, and keeping up with rapidly evolving technologies. It can also be daunting to translate data findings into actionable business insights for stakeholders who may not have a technical background. To overcome these challenges, it's helpful to develop strong communication skills, seek mentorship from experienced colleagues, and stay updated on industry best practices and compliance standards. Regular collaboration with cross-functional teams, such as engineering and product, is also essential for building a solid foundation and advancing in the fintech sector.

What is the difference between Entry Level Fintech Data Scientist vs Entry Level Data Analyst?

AspectEntry Level Fintech Data ScientistEntry Level Data Analyst
Required CredentialsBachelor's in Data Science, Computer Science, or related field; knowledge of programming languages like Python or RBachelor's in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and basic data visualization tools
Work EnvironmentFinancial technology companies, banks, or fintech startups; focus on developing predictive models and algorithmsVarious industries including finance, marketing, and healthcare; focus on data reporting and basic analysis
Employer & Industry UsageCommonly employed in fintech firms to build data-driven products and servicesWidely used across industries for business insights and reporting

In summary, an Entry Level Fintech Data Scientist typically requires programming skills and focuses on building models within fintech companies, while an Entry Level Data Analyst emphasizes data reporting and visualization across various industries. Both roles serve different analytical needs but share foundational data skills.

What are the most commonly searched types of Fintech Data Scientist jobs in California?

The most popular types of Fintech Data Scientist jobs in California are:

What cities in California are hiring for Entry Level Fintech Data Scientist jobs?

Cities in California with the most Entry Level Fintech Data Scientist job openings:

Infographic showing various Entry Level Fintech Data Scientist job openings in California as of August 2026, with employment types broken down into 33% Full Time, 33% Part Time, and 34% Contract. Highlights an 100% In-person job distribution.

Data Scientist

Triumph

San Francisco, CA • On-site

Full-time

Re-posted 21 days ago


Job description

Job Summary:
Triumph is a company focused on creating engaging products for real-money players. As a Data Scientist, you will develop models that influence pricing, user retention, and monetization strategies, while collaborating with a small quant team to drive impactful business decisions.
Responsibilities:
• Develop and optimize the pricing engines, payout structures, and edge calculations that are the mathematical backbone of Triumph's revenue.
• Build models that map the full player lifecycle: acquisition, activation, engagement, monetization, churn risk.
• Design and analyze experiments (A/B tests and beyond) with rigorous statistical methodology.
• Develop ML and statistical models on rich, high-frequency user behavior data (session patterns, spend curves, matchmaking outcomes, gameplay trajectories).
• Build models that directly inform acquisition spend and channel optimization, connecting upstream marketing decisions to downstream LTV and monetization outcomes.
• Partner closely with engineering, product, and leadership to translate model outputs into shipped features and strategic decisions.
Qualifications:
Required:
• Bachelor's degree in a quantitative subject: math, physics, computer science, statistics, economics, or a related discipline.
• True depth and mastery in at least one quantitative domain: probability, statistics, applied ML, causal inference, or mathematics.
• Proficiency in Python and SQL.
• Experience working with large-scale user or behavioral datasets.
Preferred:
• Experience in consumer tech, gaming, fintech, or marketplace data science, particularly in monetization, LTV modeling, or experimentation.
• Prior experience as a quantitative trader or quantitative researcher.
• Experience in competitive math, physics, or CS olympiads, or a graduate degree in a quantitative discipline.
• Nationally competitive in any activity.
Company:
Triumph is a game-developing software firm. Founded in 2020, the company is headquartered in San Francisco, USA, with a team of 51-200 employees. The company is currently Early Stage.