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

Required Qualifications:

• Experience in Systems Engineering, and Technology Architecture, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Strong experience in applied data science, operations analytics, or data governance in a complex technology or financial environment
• Proficiency in Python, SQL, and data manipulation libraries
• Ability to work with structured and unstructured data, building models that drive insight and action
• Strong understanding of dashboards and visualisation (Power BI, Tableau, or similar)
• Experience in building frameworks for operational visibility, risk tracking, or performance measurement
• Familiarity with SRE/production engineering metrics (e.g. MTTR, incident volume, tech debt) and their business contex

Wells Fargo is seeking a Data Scientist to join the Payments & Liquidity Technology (GPLT) with a focus on enabling data-driven decision making across Service Operations, Problem Management, Change, Resilience, and other key functions within Platform.
This role will support the development of platform-wide data governance frameworks, enhance visibility into operational health, and improve how demand and utilisation are tracked across teams. You will work horizontally across all platform functions to identify gaps, surface trends, and build the insight layer that enables leadership to act faster, smarter, and more proactively.

In this role, you will:

• Develop and run platform-wide data frameworks and dashboards that provide a single source of truth for Service Management, demand, and stability metrics
• Support the build-out of hotspot tracking and thematic analysis to proactively identify and prioritise operational risk
• Design and implement data models that help assess team utilisation, demand vs. capacity alignment, and control health
• Partner with function leads (Ops, Problem, Transition, Automation) to embed data governance into BAU routines
• Help mature the use of AI/ML and automated analytics to drive insight from unstructured operational data (e.g. incident tickets, changes, tech debt)
• Create intuitive visualisations that support senior management, and platform leads in making informed, data-backed decisions
• Ensure data pipelines, sources, and reporting processes are governed, repeatable, and auditable

Required Qualifications:

• Experience in Systems Engineering, and Technology Architecture, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Strong experience in applied data science, operations analytics, or data governance in a complex technology or financial environment
• Proficiency in Python, SQL, and data manipulation libraries
• Ability to work with structured and unstructured data, building models that drive insight and action
• Strong understanding of dashboards and visualisation (Power BI, Tableau, or similar)
• Experience in building frameworks for operational visibility, risk tracking, or performance measurement
• Familiarity with SRE/production engineering metrics (e.g. MTTR, incident volume, tech debt) and their business context