Description

Why Wells Fargo

Are you looking for more? Find it here. At Wells Fargo, we're more than a financial services leader - we're a global trailblazer committed to driving innovation, empowering communities, and helping our customers succeed. We believe that a meaningful career is much more than just a job - it's about finding all of the elements to help you thrive, in one place.

Living the Well Life means you're supported in life, not just work. It means having robust benefits, competitive compensation, and programs designed to help you find work-life balance and well-being. You'll be rewarded for investing in your community, celebrated for being your authentic self, and empowered to grow. And we're recognized for it - Wells Fargo once again ranked in the top three - making us the #1 financial services employer - on the 2025 LinkedIn Top Companies list of best workplaces "to grow your career" in the U.S. Join us!

About this role:

Wells Fargo is seeking a Lead 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 outcomes.

In 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 business, technology, data, and risk teams to move analytically sound fraud solutions into production and ensure models continue to address changing fraud threats.
• Mentor or provide technical leadership to quantitative analysts and contribute to standards, reusable modeling practices, and a culture of analytical rigor.
• Perform targeted analysis and reporting in response to emerging fraud trends, model-performance questions, audit requests, or regulatory review needs.

What makes this opportunity compelling

• Work on complex, high-value fraud problems where advanced analytics can directly strengthen customer protection.
• Influence both model innovation and model integrity through hands-on development, validation, governance, and production partnership.
• Collaborate with quantitative, fraud, technology, risk, audit, and business professionals across a large-scale financial services environment.
• Help shape how machine learning, AI, and emerging GenAI capabilities are responsibly applied to fraud detection and prevention.

Required Qualifications:

• 5+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Desired Qualifications:

• Master's degree or higher in a quantitative discipline.
• 3+ years of fraud detection modeling or analytical experience within 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 model 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, AutoEncoder, SVM, Reinforcement Learning, Isolation Forest, Random Forest, XGBoost, or GBM.
• Experience evaluating fraud models using techniques and measures appropriate to imbalanced classification, model discrimination, calibration, stability, threshold selection, false-positive trade-offs, and out-of-time performance.
• Experience with NLP, LLMs, and GenAI, with the ability to assess appropriate 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 to high-quality work.

Job Expectations:

• This position is NOT eligible for Visa sponsorship.
• Ability to work on site per Wells Fargo's standard operating model in the listed location (hybrid schedule - 3 days on-site & 2 remote).
• The ability to travel up to 10% of the time.

Posting Location:

• Charlotte, NC

** The Chief Operations Office (COO) functions adhere to a location strategy; therefore, your candidacy may be determined based on your current location. Remote work locations are not available for these roles, so if you are not in a location listed on the posting, you must commit to self-relocation within an agreed upon timeframe. **

Posting End Date:

26 Oct 2026

*Job posting may come down early due to volume of applicants.

We Value Equal Opportunity

Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements.

Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.

Applicants with Disabilities

To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.

Drug and Alcohol Policy

Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.

Wells Fargo Recruitment and Hiring Requirements:

a. Third-Party recordings are prohibited unless authorized by Wells Fargo.

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.