Senior Lead Data Science Consultant

Wells Fargo Bank•Charlotte, NC

17h•Hybrid

About The Position

Wells Fargo is seeking a Senior Lead Data Science Consultant within their analytics team for Business Banking. In this role, you will lead the strategy and execution of highly complex, large-scale data, analytics, and AI initiatives that span multiple business and technology functions. You will translate ambiguous business needs into clear problem statements, scope, roadmaps, workplans, data requirements, success measures, and implementation approaches. You will own end-to-end initiative delivery from discovery and prioritization through implementation, adoption, and measurement of business value. You will coordinate dependencies, decisions, risks, issues, and escalations across business, data, technology, model development, risk, legal, compliance, and operations partners. You will evaluate data availability, quality, controls, technological readiness, and resource requirements needed to deliver scalable AI and analytics solutions. You will establish repeatable delivery frameworks, governance routines, and executive reporting that improve transparency, accountability, and time to value. You will act as an advisor to senior leadership to develop or influence data and AI strategies for highly complex business and technical needs. You will provide vision, direction, and expertise on innovative business solutions that align with enterprise priorities, risk considerations, and customer outcomes. You will build and maintain trusted relationships across a matrixed organization, reconcile competing priorities, and drive timely decisions and accountability. You will translate data and analytical findings into clear recommendations, tradeoffs, investment choices, and action plans for business and technology leaders. You will identify opportunities to apply AI, machine learning, automation, and advanced analytics to improve decision-making, productivity, controls, and client outcomes. You will serve as the primary point of coordination for senior business, data, technology, risk, and operations stakeholders across assigned initiatives. You will develop executive-ready communications that clearly articulate strategy, progress, business impact, decisions, risks, dependencies, and recommended actions. You will facilitate senior leader and governance forums, tailoring complex data and AI concepts for technical and non-technical audiences. You will influence without direct authority, resolve misalignment, and mobilize cross-functional teams around shared outcomes. You will respond to ad hoc leadership requests with concise, high-impact analysis, sound judgment, and clear recommendations. You will ensure initiatives adhere to applicable data management, model risk, privacy, information security, legal, compliance, and responsible AI requirements. You will embed appropriate controls, documentation, traceability, and monitoring throughout the initiative lifecycle. You will partner with control functions to identify risks early, clarify approval paths, and drive timely remediation. You will promote high standards for data quality, analytical rigor, explainability, and sustainable adoption.

Requirements

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.
Significant experience leading complex data, analytics, AI, machine learning, or enterprise transformation initiatives from strategy through implementation.
Demonstrated program or project leadership across multiple concurrent, interdependent workstreams in a large, matrixed organization.
Deep understanding of enterprise data concepts, including data sourcing, quality, lineage, integration, governance, analytics, and operationalization.
Strong partner management skills, with a track record of building alignment and influencing business, data, technology, risk, and operations stakeholders without direct authority.
Executive-level written and verbal communication skills, including experience presenting complex topics, choices, risks, and recommendations to senior leaders and governance forums.
Ability to structure ambiguous problems, establish clear priorities, manage dependencies and escalations, and deliver outcomes under aggressive timelines.
Strong business acumen and judgment, with the ability to connect data and AI investments to measurable business value, enterprise goals, and risk considerations.

Nice To Haves

7+ years of experience in data, analytics, AI or machine learning, data engineering, product or program management, or enterprise transformation.
Experience leading AI-enabled capabilities, including use-case development, data readiness, model development or integration, implementation, adoption, and value measurement.
Working knowledge of modern data and AI ecosystems, such as cloud platforms, machine learning operations, large language models, generative AI, workflow automation, APIs, and business intelligence tools.
Hands-on fluency with analytical tools such as SQL, Python, R, SAS, Power BI, Tableau, or Alteryx sufficient to challenge assumptions and partner effectively with technical teams.
Experience in banking or financial services and familiarity with data, model, technology, operational risk, and regulatory expectations.
Experience defining initiative objectives, key results, roadmaps, delivery plans, operating routines, and executive-level performance reporting.
Bachelor’s or advanced degree in Data Science, Analytics, Computer Science, Engineering, Statistics, Mathematics, Business, or a related discipline.

Responsibilities

Lead the strategy and execution of highly complex, large-scale data, analytics, and AI initiatives.
Translate ambiguous business needs into clear problem statements, scope, roadmaps, workplans, data requirements, success measures, and implementation approaches.
Own end-to-end initiative delivery from discovery and prioritization through implementation, adoption, and measurement of business value.
Coordinate dependencies, decisions, risks, issues, and escalations across business, data, technology, model development, risk, legal, compliance, and operations partners.
Evaluate data availability, quality, controls, technological readiness, and resource requirements needed to deliver scalable AI and analytics solutions.
Establish repeatable delivery frameworks, governance routines, and executive reporting that improve transparency, accountability, and time to value.
Act as an advisor to senior leadership to develop or influence data and AI strategies for highly complex business and technical needs.
Provide vision, direction, and expertise on innovative business solutions that align with enterprise priorities, risk considerations, and customer outcomes.
Build and maintain trusted relationships across a matrixed organization, reconcile competing priorities, and drive timely decisions and accountability.
Translate data and analytical findings into clear recommendations, tradeoffs, investment choices, and action plans for business and technology leaders.
Identify opportunities to apply AI, machine learning, automation, and advanced analytics to improve decision-making, productivity, controls, and client outcomes.
Serve as the primary point of coordination for senior business, data, technology, risk, and operations stakeholders across assigned initiatives.
Develop executive-ready communications that clearly articulate strategy, progress, business impact, decisions, risks, dependencies, and recommended actions.
Facilitate senior leader and governance forums, tailoring complex data and AI concepts for technical and non-technical audiences.
Influence without direct authority, resolve misalignment, and mobilize cross-functional teams around shared outcomes.
Respond to ad hoc leadership requests with concise, high-impact analysis, sound judgment, and clear recommendations.
Ensure initiatives adhere to applicable data management, model risk, privacy, information security, legal, compliance, and responsible AI requirements.
Embed appropriate controls, documentation, traceability, and monitoring throughout the initiative lifecycle.
Partner with control functions to identify risks early, clarify approval paths, and drive timely remediation.
Promote high standards for data quality, analytical rigor, explainability, and sustainable adoption.