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Fraud Detection Machine Learning Jobs in Texas (NOW HIRING)

Braviant Holdings

Senior Fraud Risk Analyst

Dallas, TX · On-site

... machine learning to transform how people access credit online. Our next-generation approach to ... You will be responsible for identifying fraud patterns, building detection strategies, and ...

Braviant Holdings

Senior Fraud Risk Analyst

Dallas, TX · On-site +1

... machine learning to transform how people access credit online. Our next-generation approach to ... You will be responsible for identifying fraud patterns, building detection strategies, and ...

TigerGraph

Account Executive

Austin, TX · On-site

$150K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

East West Bank

Financial Crimes Data Scientist

Houston, TX · On-site

$100 - $200/hr

Overview Financial Crimes Data Scientist specializing in the development, deployment, and optimization of in-house AML and fraud detection models. Leverages machine learning, network analytics, and ...

Photon

Sr Business Analyst - Irving,TX

Irving, TX · On-site

$88K - $114K/yr

Collaborate with data and technology teams to identify opportunities where AI or machine learning can enhance operational processes, such as fraud detection, customer support automation, or ...

Photon

Sr Business Analyst - Irving,TX

Irving, TX · On-site

$88K - $114K/yr

Collaborate with data and technology teams to identify opportunities where AI or machine learning can enhance operational processes, such as fraud detection, customer support automation, or ...

Photon

Sr Business Analyst - Irving,TX

Irving, TX · On-site

$88K - $114K/yr

Collaborate with data and technology teams to identify opportunities where AI or machine learning can enhance operational processes, such as fraud detection, customer support automation, or ...

Photon

Sr Business Analyst - Irving,TX

Irving, TX · On-site

$88K - $114K/yr

Collaborate with data and technology teams to identify opportunities where AI or machine learning can enhance operational processes, such as fraud detection, customer support automation, or ...

SoFi

Fraud Model Developer

Frisco, TX · On-site

Familiarity with graph databases, graph analytics, or network-based fraud-detection methods. * Experience developing, deploying, or productionizing machine learning models in an AWS environment.

Q2

Senior Fraud Response Data Analyst

Austin, TX · On-site

We invest in the growth and development of our team members through ongoing learning opportunities ... By anticipating how fraudsters may bypass controls, evade detection rules, or exploit process ...

Cypress HCM

Data Science Engineer

Austin, TX · Hybrid

$65 - $69.72/hr

Fraud ADUS Key Responsibilities: * Develop and implement end-to-end ML models for fraud detection ... Advanced degrees are a plus. * 5 years of experience in data science and machine learning.

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

Fine‑tune and deploy computer vision and deep learning models for object detection, object ... Contribute to our machine learning repositories and optimize models for performance, scalability ...

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Fraud Detection Machine Learning information

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How much do fraud detection machine learning jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for fraud detection machine learning in Texas is $16.82, according to ZipRecruiter salary data. Most workers in this role earn between $13.89 and $17.93 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.

What are popular job titles related to Fraud Detection Machine Learning jobs in Texas?

For Fraud Detection Machine Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Fraud Detection Machine Learning jobs in Texas look for?

The top searched job categories for Fraud Detection Machine Learning jobs in Texas are:

What cities in Texas are hiring for Fraud Detection Machine Learning jobs?

Cities in Texas with the most Fraud Detection Machine Learning job openings:

Fraud Detection Machine Learning jobs near you

Senior Fraud Risk Analyst

Braviant Holdings

Dallas, TX • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 3 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 who move fast and hold ourselves accountable for real outcomes. Everyone here rolls up their sleeves - including this role.
About the Role
We are building and scaling a high-performance consumer lending platform and are looking for a Fraud Risk Analyst to help protect the business from identity fraud, first-party fraud, and credit abuse. This role sits at the intersection of fraud, credit, and analytics, and will directly impact early loss performance and portfolio quality. You will be responsible for identifying fraud patterns, building detection strategies, and implementing controls that prevent bad actors from entering the portfolio. This is a hands-on, high-impact role suited for someone who is analytical, detail-oriented, and biased toward action, not just case review. You will work closely with Credit, Product, Operations and Engineering to ensure fraud risk is properly identified and separated from credit risk in decisioning.
What You'll Be Doing
  • Analyze application and early performance data to identify fraud patterns, including synthetic identity, first-party fraud, and credit abuse.
  • Develop and implement fraud detection strategies, including rules, thresholds, and decisioning logic.
  • Monitor early performance (e.g., FPD, zero-pay accounts) to identify potential fraud-driven losses.
  • Distinguish fraud risk vs credit risk, improving approval quality and reducing early loss.
  • Evaluate and optimize third-party fraud tools and data sources (e.g., identity verification, device intelligence, consortium data).
  • Design and execute tests to evaluate fraud strategies and improve detection performance.
  • Work with Product and Engineering to implement fraud rules and ensure accurate execution in production systems.
  • Investigate emerging fraud trends and proactively recommend changes to controls and policies.
  • Collaborate with Operations or servicing teams to improve fraud identification post-origination.
  • 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
  • 4-6 years of experience in fraud, risk, or analytics, preferably in fintech, lending, or financial services
  • Strong analytical skills with experience using SQL, Python, Excel, or similar tools to analyze large datasets
  • Understanding of key fraud types, including synthetic identity and first-party fraud and familiarity with fraud tools (i.e. identity verification, device fingerprinting, consortium data)
  • Experience identifying fraud patterns or working with fraud detection strategies (i.e. credit washing etc.)
  • Ability to translate analysis into clear actions (rules, controls, strategy changes) and exposure to A/B testing, experimentation frameworks, or champion/challenger strategies
  • 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 fraud 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.

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