Senior Quantitative Analytics Specialist – AIM Fraud Modeling

Wells Fargo Bank•Charlotte, NC

6d•Hybrid

About The Position

Wells Fargo is seeking a Senior Quantitative Analytics Specialist for its AI Innovation Modeling (AIM) group within COO Data, Analytics & AI. The position focuses on developing, validating, and advancing machine learning and statistical models that detect and prevent fraud. The specialist works with large-scale transaction and application data to identify fraud patterns, engineer predictive features, assess models, and improve their performance from research through production implementation. The role combines hands-on model development with independent validation, documentation, governance, and collaboration across fraud, technology, model risk, audit, and business teams. It includes translating technical findings for varied audiences and contributing to customer protection and business outcomes. The position follows a hybrid schedule of three days on site and two days remote in Charlotte, North Carolina. Travel may be up to 10% of the time. Remote work locations are not available, and candidates outside a listed location must commit to self-relocation within an agreed timeframe. The position is not eligible for visa sponsorship.

Requirements

At least 4 years of quantitative analytics experience, or equivalent demonstrated through work experience, training, military experience, or education.
Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science.
Ability to work on site according to Wells Fargo’s hybrid operating model: three days on site and two days remote.
Ability to travel up to 10% of the time.
Must commit to self-relocation within an agreed timeframe if not currently located in a location listed on the posting.
The position is not eligible for visa sponsorship.

Nice To Haves

Master's degree or higher in a quantitative discipline.
At least 2 years of fraud detection modeling or analytical experience in banking or financial services, including hands-on machine learning model development and/or independent model validation.
Experience developing, validating, or challenging fraud models across the lifecycle, including data assessment, sampling, feature engineering, model design and specification, performance testing, implementation review, documentation, and ongoing monitoring.
Hands-on experience with supervised and unsupervised learning techniques such as CNN, RNN, LSTM, autoencoders, SVM, reinforcement learning, isolation forest, random forest, XGBoost, or GBM.
Experience evaluating fraud models for imbalanced classification, discrimination, calibration, stability, threshold selection, false-positive trade-offs, and out-of-time performance.
Experience with NLP, LLMs, and GenAI, including assessing use cases, model risks, limitations, and controls.
Experience developing models with Keras, TensorFlow, or PyTorch.
Experience designing and executing models in big data environments using Hadoop, Spark, or distributed processing frameworks.
Advanced data analysis and wrangling skills using Python, pandas, PySpark, NumPy, SQL, or similar tools.
Ability to translate complex quantitative findings into clear recommendations for business leaders, model governance partners, auditors, and regulators.
Strong technical documentation, problem-solving, collaboration, and communication skills, with a detail-oriented and results-driven approach.

Responsibilities

Lead end-to-end development and enhancement of machine learning and statistical models for real-time fraud risk assessment, transaction or application ranking, and fraud prevention.
Analyze large-scale data to identify predictive fraud signals, evolving attack patterns, and opportunities to reduce fraud losses while supporting a strong customer experience.
Build Python-based modeling pipelines covering data preparation, sampling, feature engineering and selection, model training, testing, and performance evaluation.
Apply and assess advanced modeling techniques, including gradient boosting, random forests, isolation forests, support vector machines, neural networks, autoencoders, and sequence models.
Perform independent model validation and effective challenge, assessing conceptual soundness, data quality, assumptions, methodology, implementation, performance, stability, limitations, and monitoring results.
Design evaluation strategies using fraud-model measures, threshold analysis, out-of-time testing, back-testing, sensitivity analysis, and benchmarking.
Lead debugging, testing, code review, and performance tuning for models developed in Python and distributed computing environments.
Document model methodology, validation evidence, assumptions, limitations, controls, and performance outcomes in a clear, reproducible, 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 with business, technology, data, and risk teams to move fraud solutions into production and ensure models address changing threats.
Mentor or provide technical leadership to quantitative analysts and contribute to standards, reusable modeling practices, and analytical rigor.
Perform targeted analysis and reporting in response to emerging fraud trends, model-performance questions, audit requests, or regulatory review needs.
Follow applicable risk and compliance programs, policies, and procedures; proactively monitor and escalate risks and issues; and make sound risk decisions.