Senior Data Scientist, Fraud Risk Detection

Experian

. Explore complex datasets to identify fraud patterns, attack methods, and behavioral signals .

Posted 10/28/2026full-timeRemote • United StatesSenior💰 $92,562 - $160,440 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science and machine learning, with a strong foundation in supervised learning, model evaluation, and feature selection. Proficient in Python and familiar with large-scale data tools, capable of translating complex datasets into actionable insights for fraud detection.

Highest-signal resume keywords

Data Science ExperienceMachine Learning Model DevelopmentPython ProficiencyModel Evaluation MetricsFinancial Services Exposure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceMachine LearningStatistical ModelingSupervised LearningModel EvaluationFeature SelectionClassificationAnomaly DetectionData AnalysisHypothesis Testing

Soft Skills

Investigative MindsetCommunication Skills

Tools & Technologies

PythonPandasNumPyScikit-LearnPySparkAmazon Web ServicesGoogle CloudAzureDatabricksSnowflake

Industry Keywords

Fraud DetectionFinancial ServicesFinTechPaymentsData PrivacyModel DocumentationExplainabilityValidationGovernance Standards

Tech Stack

Tools & technologies

AWSAzureCloudNumpyPandasPySparkPythonScikit-LearnSnowflake

About the role

Key responsibilities & impact

Explore complex datasets to identify fraud patterns, attack methods, and behavioral signals
Translate fraud questions into testable hypotheses with senior data scientists
Help build machine learning models for fraud detection across account opening, account takeover, and identity risk
Evaluate models using technical and business metrics including precision, recall, fraud capture rate, false-positive rate, and customer friction
Develop and validate features using identity, transactional, behavioral, and other data sources
Write clean, well-tested code and work with engineering to bring models and features into production
Partner with the score monitoring team on model and feature monitoring
Support research on related client questions
Prepare analyses and communicate findings to technical and nontechnical audiences
Work with senior data scientists and engineers from problem definition through feature engineering, experimentation, and deployment

Requirements

What you’ll need

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, mathematics, economics, engineering, data science, or another quantitative discipline
Foundation in supervised learning, model evaluation, feature selection, statistical inference, classification, and anomaly detection
Proficiency in Python, with the ability to write clean, readable, and well-tested code
Familiarity with pandas, NumPy, and scikit-learn
Investigative mindset and ability to move from unusual data patterns to testable hypotheses
Familiarity with PySpark, cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools
Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry through coursework, internship, or prior work
Must comply with Experian's data privacy, model documentation, explainability, validation, and governance standards

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