Lead Data Scientist - Branch Banking Analytics

Wells Fargo•Irving, TX

5d•Hybrid

About The Position

Wells Fargo’s Branch Decision Sciences team within Digital, Data & AI is seeking a Lead Data Scientist to help drive data-informed decisions for branch banking leaders. This role identifies opportunities to improve customer growth, banker effectiveness, branch productivity, and network performance while translating complex analytics into clear, actionable business recommendations. Learn more about the career areas and lines of business at wellsfargojobs.com.

Requirements

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

Nice To Haves

Experience applying quantitative and analytical techniques to identify trends, solve business problems, and inform decision-making
Proficiency in SQL, SAS, Python; moderate statistical modeling is a plus
Working knowledge of data visualization tools, preferably Power BI or Tableau
Experience with business performance analytics and KPI development.
Financial services, retail/branch banking, or customer analytics experience
Ability to translate complex data into clear, compelling narratives and build productive working relationships across business and technical teams

Responsibilities

Conduct advanced analysis on branch performance and banker productivity
Build and optimize banker and branch-level KPIs, to evaluate performance and activity trends, and identify gaps and opportunities
Identify drivers of sales, customer engagement, retention, and operational efficiency
Partner with branch stakeholders, finance, and product teams to execute on strategy
Influence cross-functional stakeholders and partners
Lead moderately complex analytics initiatives independently, using complex large structured and unstructured data sets from various sources
Design experiments and measurement frameworks to quantify business outcomes.
Synthesize complex data into clear, actionable insights and recommendations, and communicate effectively through compelling executive-level storytelling, high-quality presentations, data visualizations, and impactful dashboards
Define analytical approaches rather than only executing them
Balance business priorities, statistical rigor, and practical implementation
Guide analytic best practices across the team