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Experian

Data Scientist, Fraud Risk Detection

Experian is hiring a Data Scientist, Fraud Risk Detection. Level rates it ; you can apply on Experian.

Level's read on this job

  • Builds AI ●●●● is how Level rates this data scientist role: the job is building AI systems.
  • Tools and skills the posting cites include scikit-learn, Databricks and AI evaluation.
  • Level classifies it as a junior role.
  • Level lists 393 other open roles at Experian.
  • The role was posted in the last few days.

AI in this role

scikit-learnDatabricks
ai-evaluation

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

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.
  • 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

Benefits/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
  • Explore all our exciting benefits here: https://yourexperianbenefits.com/cand-index.html

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.

How we rate this

Data Scientist, Fraud Risk Detection at Experian rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI Evaluationscikit-learnDatabricks

Also common in data scientist jobs at this level

Pay

Typical data scientist job
$166k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

  1. How do you decide that one model's output is better than another's for a given task?
  2. Walk me through how you've used scikit-learn in your day-to-day work.
  3. What are the limits of Databricks that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: AI Evaluation, scikit-learn, and Databricks. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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