Fraud Risk ML Scientist — Hybrid/Remote

Experian; United States; Hybrid; USD 74,000 - 128,000; Full time

Benefits offered by this job

Bonus plan

Medical, dental, vision benefits

401K matching

Flexible work environment (remote/hyb/

Flexible time off

Job summary

Experian in the United States is seeking a data science professional to investigate new fraud patterns, build features, and train machine learning models. You will collaborate with senior data scientists and engineers across the model development lifecycle, from problem definition to deployment.

The role emphasizes producing analytics, maintaining code quality, and aligning with privacy, governance, and explainability standards.

Qualifications

• Bachelor's degree in computer science, statistics, mathematics, economics, engineering, data science, or equivalent.
• 1+ years of professional experience in data science, machine learning, analytics.
• Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as 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.
• Coursework, internship, or project experience related to fraud detection or risk modeling.
• Familiarity with PySpark and cloud platforms such as AWS, Google Cloud, Azure, Databricks, or Snowflake.
• Exposure to financial services, fintech, payments, or regulated/fraud-intensive industry.

Responsibilities

• Explore large datasets to identify fraud patterns, attack methods, and behavioral signals.
• Translate fraud questions into testable hypotheses and analytical approaches.
• Help build ML models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using precision, recall, fraud capture rate, and false-positive rate.
• Develop and validate features using identity, transactional, behavioral, and other data sources.
• Write clean, well-tested code and work with engineering to productionize models.
• Partner with the score monitoring team to set up model/feature monitoring and research client questions.
• Communicate findings to technical and nontechnical audiences.

Skills

Python

pandas

NumPy

scikit-learn

Education

Bachelor's degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline

Tools

PySpark

AWS

Google Cloud

Azure

Databricks

Snowflake

Job description

Experian in the United States is seeking a data science professional to investigate new fraud patterns, build features, and train machine learning models. You will collaborate with senior data scientists and engineers across the model development lifecycle, from problem definition to deployment.

The role emphasizes producing analytics, maintaining code quality, and aligning with privacy, governance, and explainability standards.