Fraud Model Developer

United States Digital Space LLC; Frisco (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

United States Digital Space LLC is seeking a Fraud Model Developer to build, evaluate, and monitor ML models that support data-driven fraud decisions across Personal Loans, Credit Cards, and more.

You will develop quantitative solutions to reduce fraud losses, minimize false positives, and cut operational costs, while collaborating with risk, product, and engineering teams to ensure scalable deployment and clear result communication.

Qualifications

• Five or more years of experience in fraud modeling or related field.
• Master's or PhD in Statistics, Mathematics, Economics, Engineering, CS, or equivalent experience.
• Advanced proficiency in Python and SQL for data analysis and model development.
• Experience creating analytical reports or dashboards using Tableau or similar tools.
• Experience developing and evaluating statistical and ML models (regression, trees, GBM, RF, NN).
• Hands-on knowledge of fraud-loss forecasting and risk-modeling techniques.
• Experience monitoring model performance and recalibrating models for data drift.
• Ability to translate model results into measurable business outcomes.
• Strong cross-functional collaboration skills in fast-paced environments.
• Proactive problem solver focused on results.

Responsibilities

• Develop quantitative, statistical, and ML models to reduce fraud losses and false positives.
• Aggregate, clean, and synthesize large datasets from multiple environments for model work.
• Analyze complex data to identify fraud patterns and product performance drivers.
• Design, test, validate, and recalibrate fraud models using robust methods.
• Monitor model performance and detect degradation or data drift.
• Forecast fraud losses and run scenario analyses to assess business impact.
• Automate recurring model monitoring, reporting, and dashboards.
• Investigate external risk data and industry trends for opportunities.
• Collaborate with Engineering and ML platform teams for deployment.
• Communicate findings to business and risk partners to influence decisions.
• Maintain model documentation and governance processes.

Skills

Fraud modeling

Loss forecasting

Machine learning

Python

SQL

Data analysis

Model evaluation

Education

Master's or PhD in Statistics/Math/Economics/Engineering/CS

Tools

Tableau

AWS

Python

Job description

Employee Applicant Privacy NoticeWho we are:

Shape a brighter financial future with us.

Together with our members, we’re changing the way people think about and interact with personal finance.

We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.

The role

the company is seeking a Fraud Model Developer to join our Fraud Model Development team. In this role, you will develop, evaluate, and monitor machine learning models that support data-driven fraud and risk decisions across the company’s products and services, including Personal Loans, Student Loans, Credit Cards, and Crypto.

You will build quantitative and machine learning solutions designed to reduce fraud losses, minimize false positives, lower operational costs, and protect the company members. You will also analyze model and product performance, identify key drivers of fraud losses, and translate complex findings into actionable recommendations for business and risk partners.

This role requires strong experience in machine learning, statistical modeling, data analysis, and model performance monitoring. You will work closely with Fraud Risk, Fraud Operations, Product, Engineering, Finance, Accounting, and other business teams to develop scalable fraud-modeling solutions and ensure model performance and loss trends are clearly communicated.

What you’ll do

• Develop quantitative, statistical, and machine learning models that reduce fraud losses, minimize false positives, and lower operational expenses associated with fraud complaints and disputes.
• Aggregate, clean, and synthesize large datasets from multiple data environments to support model development and analysis.
• Analyze complex datasets to identify fraud patterns, product-performance trends, and key drivers of losses across the company’s products.
• Design, test, validate, and recalibrate fraud models using appropriate statistical and machine learning methodologies.
• Monitor model performance and identify model degradation, data drift, or changes in fraud behavior.
• Conduct fraud-loss forecasting, sensitivity analyses, and scenario-based assessments to evaluate potential business impact.
• Automate recurring model-monitoring processes, analytical reporting, and dashboards.
• Investigate external risk data and industry trends to identify emerging fraud patterns and modeling opportunities.
• Partner with Engineering and machine learning platform teams to support model implementation and production deployment.
• Collaborate with Business Units, Operations, Product, Capital Markets, Finance, Accounting, and Risk partners to communicate fraud-loss expectations, model performance, and emerging trends.
• Translate technical model results into clear recommendations that improve fraud strategies, member experiences, and operational outcomes.
• Maintain model documentation and support ongoing model governance, validation, and performance-review activities.

What you’ll need

• Five or more years of experience in fraud modeling, loss forecasting, advanced quantitative modeling, machine learning, or a related field.
• A master’s or doctoral degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or another quantitative field, or equivalent relevant professional experience.
• Advanced proficiency in Python and SQL for data analysis, feature development, and machine learning model development.
• Experience creating analytical reports or dashboards using Tableau or a comparable data-visualization platform.
• Demonstrated experience developing and evaluating statistical and machine learning models, including methods such as linear regression, logistic regression, decision trees, gradient boosting, random forests, neural networks, or clustering.
• Hands‑on knowledge of fraud-loss forecasting, fraud‑reduction methodologies, or comparable risk‑modeling techniques.
• Experience monitoring model performance and recalibrating models in response to performance changes, data drift, or evolving business conditions.
• Strong analytical and problem‑solving skills, with the ability to evaluate complex datasets and communicate meaningful conclusions.
• Ability to translate model results into measurable business outcomes, including fraud-loss reduction, false-positive improvement, member-friction reduction, or operational savings.
• Demonstrated ability to work collaboratively across technical and nontechnical teams in a complex, fast-moving environment.
• A proactive approach to identifying problems, driving change, learning new methodologies, and taking ownership of results.

Nice to have

• Experience developing fraud models within financial services, fintech, banking, lending, payments, or digital assets.
• Familiarity with graph databases, graph analytics, or network-based fraud-detection methods.
• Experience developing, deploying, or productionizing machine learning models in an AWS environment.
• Familiarity with machine learning operations, model governance, or automated model-monitoring frameworks.

Compensation and Benefits

The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.

To view all of our comprehensiveand competitivebenefits, visit ourBenefits at the companypage!

the company provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.

The Company hires the best qualified candidate for the job, without regard to protected characteristics.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.