Senior Analyst, Analytics Development & Model Governance - Experian

The Fraud Analytics and Commercialization team drives Experian 's fraud analytics business through four integrated functions: pre-sales engagement, scalable and custom solutions, consulting, and operational enablement, with the goal of becoming the industry's provider-of-choice.

We're looking for a Senior Analyst, Analytics Development and Model Governance, with a foundation in statistics and applied machine learning across the full modeling lifecycle, from initial development through validation, deployment, and ongoing governance, to join our Fraud Analytics team. As the ideal candidate, you are as comfortable interrogating model performance as building and refining models, take pride in the standards that keep production models trustworthy, and communicate findings to both technical and non-technical audiences. Core skills include navigating ambiguity, an impact-focused mindset, critical thinking, empathy, humility, and an eagerness to collaborate with data scientists, engineers, and product partners. You will be curious about the latest tools and AI solutions, evaluate their potential, and help bring the best of them into the team's workflows. Candidates who have taken an unconventional path and demonstrated the curiosity to figure things out without a blueprint will find this role and this team to be a good fit.

You will report to the Data Modeling Director.

You'll have the opportunity to:

• Develop, validate, and optimize statistical and machine learning models, including regression and XGBoost, to detect and prevent fraud.
• Design and implement features to improve model performance and stability.
• Build tools and frameworks to support scalable model development, evaluation, and monitoring.
• Evaluate model performance and conduct comparative analyses across approaches.
• Perform exploratory data analysis and assess data quality across diverse datasets.
• Enrich and integrate data from internal and external sources.
• Package, deploy, and support models in production environments.
• Conduct model governance activities, including validation, documentation, and ongoing monitoring.
• Produce reports, visualizations, and summaries to support decision-making.
• 2+ years of experience in data science, analytics, or a related field
• Degree (undergraduate or graduate) in Statistics, Applied Mathematics, Econometrics, or another quantitative discipline, or an equivalent combination of education and experience.
• Python skills for data analysis and machine learning, including PySpark, Polars, NumPy, and Pandas.
• Solid foundation in statistics and machine learning best practices.
• Experience with regression, XGBoost, and other core ML algorithms.
• Hands-on experience across the full model lifecycle: data ingestion, EDA, modeling, validation, and deployment.
• Experience building or supporting model development t