We are seeking a visionary and hands-on Executive Director-level Senior Lead Data Scientist to join our Global Payments & Liquidity (GPL) team. In this role, you will work with senior leaders to uncover insights, solve complex problems, and build data-driven solutions that fuel growth, improve products, and deepen customer engagement.
You will analyze product portfolios, identify strategic opportunities, and design solutions that deepen customer relationships and accelerate GPL’s growth.

REQUIRED QUALIFICATIONS:

• 7+ years of data science experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 5+ years of SQL
• 5+ years of Python experience
• 3+ years of experience in a leadership or mentorship role
• Master’s degree level or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, or computer science

DESIRED QUALIFICATIONS:

• Strong understanding of banking products and services, including Treasury/Transaction Services
• Knowledge of cloud platforms (e.g., AWS, Azure) and data visualization tools (e.g., Power BI, Tableau).

APPLICANTS WITH DISABILITIES

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

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

• Partner and consult with business leaders to understand strategic goals and translate them into data science initiatives.
• Lead the design and implementation of complex solutions and deliverables by utilizing advanced statistical techniques and Machine learning models.
• Collaborate with data engineering and business analysts to ensure scalable and secure deployment of analytics solutions.
• Enforce and establish best practices related to data quality and governance.
• Ensure data quality, model accuracy, and reproducibility through rigorous validation and documentation.
• Communicate insights and recommendations clearly to both technical and non-technical stakeholders.