Expert Data Scientist, Fraud Risk Detection

Experian

. Investigate large datasets through exploratory analysis and fraud label development .

Posted 10/13/2026full-timeRemote • United StatesMid-LevelSenior💰 $103,669 - $179,693 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and validating machine learning models for fraud detection, utilizing Python and PySpark for data analysis and model implementation. Proficient in evaluating model performance and ensuring compliance with data privacy and governance standards.

Highest-signal resume keywords

Machine Learning Model DevelopmentFraud Detection ExpertisePython ProgrammingModel Evaluation TechniquesData Analysis and Feature Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingData ScienceFraud DetectionFeature EngineeringModel EvaluationPythonPySparkPandasNumPy

Tools & Technologies

Scikit-learnXGBoostTensorFlowDistributed Data SystemsCloud Data Systems

Industry Keywords

Fraud PatternsIdentity RiskCredit RiskFinancial CrimeModel DriftData PrivacyModel Documentation

Tech Stack

Tools & technologies

CloudNumpyPandasPySparkPythonScikit-LearnTensorflow

About the role

Key responsibilities & impact

Investigate large datasets through exploratory analysis and fraud label development
Identify fraud patterns, attack methods, and behavioral signals
Translate ambiguous fraud and risk problems into hypotheses, analytical plans, model requirements, and measurable success criteria
Develop machine learning models for account opening, account takeover, and identity risk fraud detection
Evaluate models using 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
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 covering model behavior, tradeoffs, risks, and recommendations
Follow standards for data privacy, model documentation, explainability, validation, and governance
Report to the Senior Manager of Fraud Analytics
Work with senior data scientists and engineers from problem definition through feature engineering, experimentation, and deployment

Requirements

What you’ll need

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
Experience writing modular and tested code for large datasets and distributed or cloud data systems
Experience with pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies
Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration
Experience with class imbalance, delayed or incomplete labels, changing attack patterns, and model drift
Experience moving models beyond experimentation into production
Compliance with applicable work authorization and employment requirements in the United States

Benefits

Comp & perks

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
Comprehensive benefits package
Variable pay opportunity
Inclusive and purpose-driven culture
Accommodation support for disabilities or special needs