Data Scientist, Fraud Risk Detection

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

• Job Posting - Salary Range: $73,789 - $127,901
• Department: Analytics
• Flexible Time Off: 15 Days
• Schedule: Full Time
• Shift: Day Shift
• Compensation: USD 73,789 - USD 127,901 - 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.

At Experian, we believe great work happens when people come together. We're focused on attracting talent in and around our designated hub locations who are open to collaborating with colleagues in person. Aligning employees to these talent markets helps foster collaboration, strengthen team connections, and support long-term career growth within Experian. This is a hybrid position and you will reside in or near one of our collaboration hubs:

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.

You will help investigate new fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers throughout the model development lifecycle, from problem definition and data assembly through feature engineering, experimentation, and deployment.

Your work will contribute to the techniques, analytical tools, and capabilities Experian's fraud analytics team has spent years refining. This is an individual contributor position and you will report into the Sr. Manager of Fraud Analytics

What you'll do

• Explore large datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals.
• Work with senior data scientists to translate fraud questions into testable hypotheses and analytical approaches.
• Help build machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction.
• Develop and validate features using identity, transactional, behavioral, and other available data sources.
• Write clean, well-tested code, and work with engineering to bring models and features into production.
• Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions.
• Help prepare analyses and communicate findings to both technical and nontechnical audiences.
• Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance.

Qualifications

• Bachelor's degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline - or equivalent experience
• 1+ years of professional experience in data science, machine learning, statistical modeling, or advanced 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 common data science and machine-learning tools such as pandas, NumPy, and scikit-learn.
• Coursework, internship, or project experience related to fraud detection, risk modeling, or another adversarial or classification-style problem.
• Familiarity with PySpark, cloud platforms such as AWS, 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.
• #LI-Hybrid

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.