Senior Quantitative Analytics Specialist – AIM Fraud Modeling

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About this role:

Wells Fargo is seeking a Senior Quantitative Analytics Specialist within the AI Innovation Modeling (AIM) group under COO Data, Analytics & AI. This is a high-impact opportunity for an experienced quantitative professional who is motivated by developing, validating, and advancing machine learning models that detect and prevent fraud.

You will work with large-scale transaction and application data to uncover emerging fraud patterns, engineer predictive features, develop and assess sophisticated models, and strengthen model performance from research through production implementation. The role combines hands-on model development with rigorous validation, documentation, governance, and partnership across fraud, technology, model risk, audit, and business teams.

This role is ideal for someone who enjoys solving complex analytical problems, challenging model assumptions, translating technical findings for varied audiences, and seeing their work improve customer protection and business  this role, you will:

Lead the end-to-end development and enhancement of machine learning and statistical models used for real-time fraud risk assessment, transaction or application ranking, and fraud prevention.

Analyze millions to billions of records in big data environments to identify predictive fraud signals, evolving attack patterns, and opportunities to reduce fraud losses while supporting a strong customer experience.

Build robust Python-based modeling pipelines, including data preparation, sampling, feature engineering, feature selection, model training, testing, and performance evaluation.

Apply and assess advanced techniques such as gradient boosting, random forests, isolation forests, support vector machines, neural networks, autoencoders, sequence models, and other fit-for-purpose approaches.

Perform independent model validation and effective challenge by evaluating conceptual soundness, data quality, assumptions, methodology, implementation, performance, stability, limitations, and ongoing monitoring results.

Design evaluation strategies using appropriate fraud-model measures, threshold analysis, out-of-time testing, back-testing, sensitivity analysis, and benchmarking to confirm models are reliable and fit for purpose.

Lead debugging, testing, code review, and performance tuning for machine learning and statistical models developed in Python and distributed computing environments.

Document model methodology, validation evidence, assumptions, limitations, controls, and performance outcomes in a clear, reproducible, and governance-ready manner.

Present analytical findings and recommendations to technical and non-technical audiences, including fraud leaders, business stakeholders, model validation, model risk, internal audit, governance partners, and regulatory reviewers.

Partner across…