Expert Data Modeler, Fraud Risk Detection

SmartRecruiters, Inc.; Northern (KY); Hybrid; USD 104,000 - 180,000; Full time

Benefits offered by this job

Bonus plan

Medical, dental, vision

401K matching

Flexible work environment (remote/hybr

Job summary

Experian is seeking a Data Scientist for Fraud Analytics & Commercialization. The role focuses on building fraud detection models and features to identify high-risk activity while minimizing friction for legitimate customers. You will collaborate with senior data scientists and engineers from problem definition to deployment, delivering production-ready solutions.

The position reports to the Sr. Manager of Fraud Analytics and is remote, with a strong emphasis on research, experimentation, and

Qualifications

• At least 3 years of experience in data science, ML, or a related quantitative field.
• Bachelor’s or advanced degree in computer science, statistics, engineering, data science, or another quantitative discipline.
• Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime, or other adversarial risk models.

Responsibilities

• Investigate large datasets, including exploratory analysis and fraud label development, to identify latest fraud patterns, attack methods, and behavioral signals.
• Translate ambiguous fraud and risk problems into clear hypotheses, analytical plans, model requirements, and measurable success criteria.
• Develop machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using metrics like ROC/AUC/KS/Gini, precision/recall, fraud capture rate, false-positive rate, customer friction, and fraud losses prevented.
• Develop and validate predictive features using identity, transactional, consumer credit history, device, behavioral, temporal, velocity, network, and third-party data.
• Write clean, efficient, well-tested Python and PySpark code, and collaborate with teams to bring models and features into batch, retro, or real-time decisioning environments.
• Monitor feature quality, model performance, population changes, and fraud-pattern drift
• Design and present analyses for model behavior, tradeoffs, risks, and recommendations
• Follow appropriate standards for data privacy, model documentation, explainability, validation, and governance.

Skills

Python

PySpark

Fraud detection experience

Model deployment

Experimentation

Data visualization

Education

Bachelor’s or advanced degree in CS, statistics, engineering, data science

Tools

Pandas

NumPy

scikit-learn

XGBoost

TensorFlow

Job description

• Full-time
• Employee Status: Regular
• Role Type: Home
• Job Posting - Salary Range: $103,669 - $179,693
• Department: Analytics
• Flexible Time Off: 20 Days
• Schedule: Full Time
• Shift: Day Shift
• Compensation: USD 103,669 - USD 179,693 - yearly

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Job Description

Overview

Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms.

We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model.

You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions.

We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization.

This is a remote role and you will report into the Sr. Manager of Fraud Analytics.

What you'll do

• Investigate large datasets, including exploratory analysis and fraud label development, to identify latest fraud patterns, attack methods, and behavioral signals.
• Translate ambiguous fraud and risk problems into clear hypotheses, analytical plans, model requirements, and measurable success criteria.
• Develop machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using metrics like ROC/AUC/KS/Gini, precision/recall, fraud capture rate, false-positive rate, customer friction, and fraud losses prevented.
• Develop and validate predictive features using identity, transactional, consumer credit history, device, behavioral, temporal, velocity, network, and third-party data.
• Write clean, efficient, well-tested Python and PySpark code, and collaborate with teams to bring models and features into batch, retro, or real-time decisioning environments.
• Monitor feature quality, model performance, population changes, and fraud-pattern drift
• Design and present analyses for model behavior, tradeoffs, risks, and recommendations
• Follow appropriate standards for data privacy, model documentation, explainability, validation, and governance.

Qualifications

Qualifications

• At least 3 years of experience in data science, machine learning, statistical modeling, or a related quantitative field
• Bachelor's or advanced degree in computer science, statistics, engineering, data science, or another quantitative discipline
• Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime, or other adversarial risk models.
• Demonstrated experience creating meaningful fraud features
• Proficiency in Python and PySpark, with experience writing modular and tested code for large datasets and distributed or cloud data systems.
• Experience using common data science and machine-learning tools such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies.
• Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration.
• Experience navigating challenges common to fraud modeling, including class imbalance, delayed or incomplete labels, changing attack patterns, and model drift.
• Experience moving models beyond experimentation and into production, either directly or in close partnership with engineering teams.

Additional Information

• Great compensation package and bonus plan
• Core benefits including medical, dental, vision, and matching 401K
• Flexible work environment, ability to work remote, hybrid or in-office
• Flexible time off including volunteer time off, vacation, sick and 12-paid holidays

Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose-driven culture is multi award-winning. We have won World's Best Workplaces 2025 (Fortune Global Top 25), and Great Place To Work in 26 countries to name a few.

Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @ experian.com email address. Experian will never ask candidates to make any payment as part of a recruitment process.

Our compensation reflects the cost of labor across several U.S. geographic markets. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. You will be eligible for a variable pay opportunity and a comprehensive benefits package.

Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

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