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Fintech Data Science Jobs in Texas (NOW HIRING)

FloatMe

Senior/Staff Data Scientist

Austin, TX · On-site

... moving fintech team, this is the seat for you. What You'll Do * You will turn complex product ... A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science ...

FloatMe

Senior/Staff Data Scientist

San Antonio, TX · On-site

... moving fintech team, this is the seat for you. What You'll Do * You will turn complex product ... A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science ...

Spgi

$100K - $149K/yr

Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field, or ... Background in financial services, fintech, or other regulated industries with a strong ...

Thatch Health

Data Scientist, Analytics

Austin, TX · On-site

$160K - $200K/yr

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 ...

Vericast

Senior Data Engineer

San Antonio, TX · On-site

$130K - $150K/yr

The Senior Data Engineer will work closely with our analytics, reporting, and data science teams on ... Experience working within a Financial Institution (FI), Banking, FinTech, MarTech, AdTech ...

Vericast

Senior Data Engineer

San Antonio, TX · On-site +1

$130K - $150K/yr

The Senior Data Engineer will work closely with our analytics, reporting, and data science teams on ... Experience working within a Financial Institution (FI), Banking, FinTech, MarTech, AdTech ...

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Fintech Data Science information

See Texas salary details

$34.9K

$114.3K

$183.1K

How much do fintech data science jobs pay per year?

As of Sep 12, 2026, the average yearly pay for fintech data science in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 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 Texas?

For Fintech Data Science jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Fintech Data Science jobs in Texas look for?

The top searched job categories for Fintech Data Science jobs in Texas are:

What cities in Texas are hiring for Fintech Data Science jobs?

Cities in Texas with the most Fintech Data Science job openings:

Fintech Data Science jobs near you
Infographic showing various Fintech Data Science job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 10% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.

Database & Data Science Engineer - Advanced Data Systems

Austin, TX • On-site

Sophelio LLC
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Posted 3 days ago

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Job description

Database & Data Science Engineer - Advanced Data Systems

Job Description

About Us

Sophelio is an AI/ML startup based in Austin, TX, building next-generation data infrastructure for domains where data complexity and scale matter most—from nuclear fusion research and scientific simulation to BCI, fintech, MLOps, and beyond.

We sit at the intersection of databases, machine learning, and advanced computation, creating systems that transform large, heterogeneous datasets into actionable intelligence. Our mission is bold: accelerate innovation across science and industry through better data architectures and workflows.

We're not looking for someone just seeking their next paycheck—we're looking for builders. People who thrive in fast-paced environments, want their work to matter, and are motivated by the chance to help shape both a company and an industry.

What you will do
  • Architect and scale non-relational and relational databases (MongoDB, SQL) for large, high-throughput datasets.
  • Build data pipelines and tooling to support ML/AI workflows (ingestion, labeling, harmonization, retrieval).
  • Collaborate with scientists, engineers, and customers to operationalize products (MLOps, DevOps).
  • Ensure scalability and efficiency of our systems as we prepare for rapid growth.
  • Contribute ideas beyond engineering - help shape product vision and company direction.
What We’re Looking For
  • Strong foundation in data science, database management, computer science, computational mathematics, and ML/AI.
  • 3+ years of professional experience in one or more related fields (e.g., database engineering, data science, ML/AI systems, computational software development).
  • Proficiency with MongoDB (PyMongo, Compass), SQL, Python (TensorFlow, PyTorch, etc.).
  • Systems-level thinking, with experience in scalable architectures and data workflows.
  • Strong problem-solving skills and clear, proactive communication.
  • Ability to work both independently and in highly collaborative teams.
  • Interest in emerging domains like fusion energy, clean energy, and other data-intensive fields.
  • Software development background; full-stack experience is a plus.
  • Familiarity with computational science, high-performance simulation data, and applied mathematics.
  • Curiosity and drive to learn quickly across different application areas.
Why Join Us
  • Impact that matters: Work on data infrastructure powering breakthroughs in science, finance, and AI.
  • Growth environment: Competitive compensation, equity opportunities, and career advancement.
  • Culture: Inclusive, ambitious, and collaborative—we value curiosity, initiative, and creativity.
  • Location: Austin HQ near Zilker Park, with flexible hybrid options.
  • Future-focused: Join a team operating at the leading edge of AI, MLOps, and large-scale data systems.
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