Pay
$118K–$195K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,828 postings in the last year. Stated ranges, not salaries paid.
United States, United States, United States
Actively hiring, first seen by us 10 hours agoExperian 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: Costa Mesa, San Diego & San Jose, CA · Allen & Austin, TX · Franklin, TN · New York, NY · Schaumburg, IL · Washington, DC · Atlanta, GA · Boston, MA · Seattle, WA
Work setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Work with senior data scientists to translate fraud questions into testable hypotheses and analytical approaches.
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.
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.
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.
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.
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 doRead the full posting
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
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.
Familiarity with PySpark, cloud platforms such as AWS, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools.
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
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$118K–$195K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,828 postings in the last year. Stated ranges, not salaries paid.
Day 0
Early. Similar jobs usually come down between day 8 and day 59.
At Experian, jobs usually come down by about day 9.
From 11,556 similar postings that came down, and 257 of Experian’s. Coming down is not always a hire.
35 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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.
- 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.
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New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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
Benefits/Perks:
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.
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:
Costa Mesa, San Diego & San Jose, CA · Allen & Austin, TX · Franklin, TN · New York, NY · Schaumburg, IL · Washington, DC · Atlanta, GA · Boston, MA · Seattle, WA
Selected passages from the saved posting. Check the full description for conditions and exceptions.
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United States, United States, United States
- Exposure to financial services, fintech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work. - #LI-Hybrid Additional Information
- 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
More relevant text appears in the full description.
No clear work-authorization passage found. Eligibility is unconfirmed.