Senior Data Modeler, Fraud Risk Detection

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. 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. We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include strategic thinking, an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model. You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions. We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization. This is a remote role and you will report into the Sr. Manager of Fraud Analytics.

What you'll do - Explore complex 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 - 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. - 1+ 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, 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. - Investigative mindset and the 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.