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Fintech Data Science Jobs in Dallas, TX (NOW HIRING)

One Park Financial

Senior Data Scientist

Plano, TX · On-site

$120 - $180/hr

... fintech. * Professional Growth : We invest in our employees' growth with continuous learning ... Master's degree in mathematics, statistics, computer science, or data science. * Experience in ...

One Park Financial

Senior Data Scientist

Plano, TX · On-site

... fintech. * Professional Growth : We invest in our employees' growth with continuous learning ... Requirements * 4-7 years of experience in Data Science and the Financial Industry, preferably in ...

One Park Financial

Senior Data Scientist

Plano, TX · On-site

... fintech. * Professional Growth : We invest in our employees' growth with continuous learning ... Requirements * 4-7 years of experience in Data Science and the Financial Industry, preferably in ...

One Park Financial

Senior Data Scientist

Plano, TX · On-site

... fintech. * Professional Growth : We invest in our employees' growth with continuous learning ... Requirements * 4-7 years of experience in Data Science and the Financial Industry, preferably in ...

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All JobsFintech Data Science JobsFintech Data Science Jobs in Dallas, TX

Fintech Data Science information

See Dallas, TX salary details

$37.3K

$121.9K

$195.2K

How much do fintech data science jobs pay per year?

As of Sep 6, 2026, the average yearly pay for fintech data science in Dallas, TX is $121,934.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,900.00 and $135,100.00 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 Dallas, TX?

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

What job categories do people searching Fintech Data Science jobs in Dallas, TX look for?

The top searched job categories for Fintech Data Science jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Fintech Data Science jobs?

Cities near Dallas, TX with the most Fintech Data Science job openings:

Fintech Data Science jobs near you
Infographic showing various Fintech Data Science job openings in Dallas, TX as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $121,934 per year, or $58.6 per hour.

Senior Data Scientist (Credit Risk)

Braviant Holdings

Dallas, TX

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 28 days ago

Job description

Title: Senior Data Scientist
Function: Credit Risk
Reports to: Head of Credit
Level: Mid-Level / Senior
Location: Addison, TX (5 days/week in-office)

Please note: This position is open to candidates within commuting distance to the DFW metro area only. Applicants must reside in Texas and be authorized to work in the United States. Applications from candidates outside of Texas will not be considered at this time. While we appreciate interest from all applicants, Braviant Holdings is unable to sponsor visas at this time.

Who We Are

Founded in 2015 and based in Chicago, IL, privately held Braviant Holdings, LLC is a leading provider of tech-enabled consumer credit products that combine breakthrough technology and cutting-edge machine learning to transform how people access credit online. Our next-generation approach to lending reduces credit barriers and creates a Path to Prime®, helping millions of underbanked consumers build credit history, reduce their cost of borrowing, and take control of their personal finances. Braviant has been named multiple times to the Inc. 5000 list of fastest growing private companies and has been recognized as a Best Place to Work.

We are a lean team of approximately 40 people. Everyone rolls up their sleeves here, including this role.

About the Role
We are building and scaling a high-performance consumer lending platform and are looking for a Senior Data Scientist to drive credit decisioning across the loan lifecycle. This role sits at the intersection of credit strategy, fraud, and analytics, directly impacting approval strategy, loss performance, and portfolio profitability. You will build and deploy models that inform key decisions around who we approve, how we price risk, and how we manage portfolio performance. This is a hands-on, high-impact role suited for someone who is business-oriented, data-driven, and biased toward action, not just model development. You will partner closely with Credit, Fraud, Servicing, Product, and Engineering to translate data into clear, actionable decisions that improve approval quality, reduce early loss, and drive sustainable growth.
What You'll Be Doing
  • Develop and deploy predictive models across the credit lifecycle (acquisition, risk, and collections) with a focus on improving approval quality and loss performance.
  • Translate model outputs and analysis into actionable credit strategy, including approval cutoffs, segmentation, and decision rules.
  • Analyze portfolio performance (FPD, delinquency, loss) to identify key drivers of deterioration and recommend targeted actions.
  • Evaluate tradeoffs between approval rate, loss, and profitability, and recommend strategies to optimize portfolio performance.
  • Distinguish fraud risk vs credit risk, improving early default performance and reducing losses.
  • Design and execute experiments (A/B tests, champion/challenger frameworks) to evaluate strategies and drive continuous improvement.
  • Work with Product and Engineering to implement decisioning logic into production systems and ensure accurate execution.
  • Monitor model and strategy performance over time, identifying drift, instability, or unintended impacts on portfolio outcomes.
  • Collaborate cross-functionally with other departments to ensure decisions align with business goals and risk appetite.
What You Will Bring

Required

  • Degree in Data Science, Applied Mathematics, Statistics, Economics, Computer Science or a related field
  • 5-7 years of professional experience in Data Science, Analytics or a related field within FinTech or online lending space.
  • Advanced proficiency in Python for programming, data analysis, and predictive modeling
  • Proficiency in SQL, Excel and experience with data visualization tools
  • Excellent knowledge in applied statistical methods and experience using various predictive machine learning techniques including: linear models, decision trees, boosting, and ensemble models
  • Knowledge of optimization, stochastic processes, experimental design, A/B testing and bootstrapping
  • Passion for keeping your skills up to date and exploring new methodologies
  • The ability to distill complex problems and analysis into a clear and concise narrative
 
Preferred
  • Experience in subprime consumer lending, fintech, payments, or another regulated financial services technology environment.
  • Hands-on experience applying AI to credit risk management
Benefits & Perks

Compensation at Braviant is competitive and commensurate with experience. Details will be discussed with qualified candidates during the interview process. In addition, we provide:

  • Comprehensive healthcare including medical, dental, and vision coverage
  • Generous paid time off, including PTO, sick time, and 13 company holidays
  • 401(k) with company contribution
  • Participation in annual discretionary bonus plan
  • Regular team and company gatherings
Braviant is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, or any other characteristic protected by applicable law.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.