Senior Lead Analytics Consultant -- Auto Pricing
Wells Fargo
- Location
- CHARLOTTE, NC
- Work model
- On-Site
- Level
- Senior
- Posted
- Aug 11, 2026
Skills
About this role
About this role: Wells Fargo is seeking a Sr. Lead Analytics Consultant for to the pricing team within Wells Fargo Auto. This role will lead the strategy, development, validation, and implementation of models that directly influence origination and servicing strategies. The individual will serve as a key thought leader and trusted advisor to cross-functional partners and senior leaders, providing recommendations that balance growth, profitability, risk objectives and regulatory compliance. 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 five on the 2026 LinkedIn Top Companies list of best workplaces “to grow your career” in the U.S. Join us! In this role, you will: Lead the development, implementation, and management of credit scorecards, collection and recovery models, and loss forecasting models that drive pricing, risk, and servicing strategies. Lead highly complex analytical and modeling initiatives, including highly visible cross-functional programs with significant impacts across Pricing, Risk, Finance and Auto Lending operations. Review, analyze and enhance sophisticated statistical models and programming solutions by leveraging large-scale data sets to generate actionable financial, risk and business insights that support strategic decision-making. Make and influence decisions regarding highly complex pricing strategies, credit loss assumptions, data modeling, and risk exposure, requiring a deep understanding of business objectives, regulatory requirements, and compliance considerations. Lead enterprise-wide monitoring and interpretation of Auto portfolio loss performance, proactively identifying emerging trends, risks, model limitations, and opportunities to improve forecasting accuracy. Serve as a trusted advisor to senior leadership by providing strategic vision, thought leadership, and expert guidance on innovative analytical solutions that have enterprise-wide implications. Apply deep expertise in credit risk, loss forecasting, and risk-based pricing within a highly regulated financial services environment to influence origination and servicing strategies within Wells Fargo Auto. Translate highly complex analyses and statistical models into clear, actionable insights and strategic recommendations for senior leadership utilizing advanced analytical capabilities (e.g. SAS, Python).
Required Qualifications
7+ years of Analytics, Reporting, Financial Modeling or Statistics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: 7+ years of experience in analytical work, including framing problems, conducting analysis, synthesizing insights, and influencing leadership decisions. Expert in credit scoring, loss forecasting and pricing strategies within regulated financial services, including experience setting enterprise level assumptions that directly impact product pricing and profitability. Expert analytical capability (e.g., SAS, Python) with the ability to translate complex model outputs into executive level insights and business decisions. Expert in statistical and advanced analytics techniques, including hypothesis testing, regression analysis, and machine learning.