Senior Lead Business Execution Consultant Data, AI & Platform Management Lead – COO AI Product

Wells Fargo•Charlotte, NC

1d•Hybrid

About The Position

Wells Fargo is seeking a strategic and technically proficient Senior Lead Business Execution Consultant to lead Process Intelligence data and AI platform advancement and enterprise automation modernization. This business-led role combines data and platform management, generative AI, architecture, process intelligence, and transformation. The role owns business outcomes, challenges technical recommendations, influences roadmaps, and ensures investments deliver measurable value, in partnership with Product, Technology, Architecture, Data, AI, Cloud, Risk, Compliance, Operations, and executive stakeholders. The successful candidate will lead the scaling of Process Intelligence ADS as an enterprise data and AI platform by expanding onboarding of priority use cases and broader organizational adoption. The role implements governance, metadata management, data lineage, observability, stewardship, and auditability capabilities; establishes platform KPIs, executive reporting, operating frameworks, and service governance processes; and assesses and prioritizes automation portfolios to develop modernization and migration roadmaps. Success will be measured by improved adoption, resiliency, scalability, operational transparency, and measurable business value. The position offers a hybrid work schedule and requires adherence to in-office attendance expectations. Travel is expected 5% of the time. The position is not eligible for visa sponsorship.

Requirements

7+ years of Business Execution, Implementation, or Strategic Planning experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination.
7+ years of experience in product, data, platform, technology strategy, analytics, or a related discipline.
7+ years of experience leading enterprise transformation and delivering data, analytics, AI, cloud, or platform modernization initiatives.

Nice To Haves

Strong knowledge of enterprise data management, governance, metadata, lineage, and cloud data ecosystems.
Working knowledge of GenAI, agentic AI, LLMs, RAG, vector databases, knowledge graphs, semantic search, API-first architecture, and enterprise automation.
Proven ability to evaluate technology tradeoffs, challenge architecture recommendations, and partner effectively with Architecture, Engineering, Product, and Data teams.
Strong executive communication, analytical thinking, stakeholder management, and influence in matrixed organizations.
Experience with process intelligence, process or task mining, workflow analytics, or operational excellence platforms.
Experience with cloud data platforms such as GCP, BigQuery, Dataplex, Databricks, or Snowflake.
Experience deploying or governing GenAI and AI observability capabilities in regulated environments.
Knowledge of platform product management, Responsible AI, model risk, and enterprise automation platforms.
Financial services experience or relevant product, project, data, process improvement, or cloud certification.

Responsibilities

Advise leadership on business execution, drive performance and initiatives, and develop information delivery or presentations for key stakeholders and senior management.
Lead the strategy and resolution of complex Business Execution challenges, delivering longer-term, large-scale solutions.
Provide vision, direction, and expertise to senior leadership on innovative, significant, large-scale, cross-organizational business solutions.
Lead team meetings or steering committees to facilitate decisions and support implementation of recommendations and plans.
Engage with professionals and managers across multiple lines of business and serve as an experienced advisor to leadership.
Provide direction to a cross-functional team using business expertise.
Lead the strategy, roadmap, governance, operating model, and adoption of COO AI Product Data.
Build scalable data onboarding, ingestion, transformation, retrieval, and consumption capabilities for GenAI, process intelligence, analytics, automation, and reporting.
Partner with AI Engineering and Data Science to enable responsible, governed enterprise AI capabilities.
Assess technologies and architecture patterns, including LLMs, RAG, agentic AI, vector databases, knowledge graphs, semantic search, observability, and orchestration.
Establish data governance, metadata, lineage, observability, auditability, and platform controls.
Define KPIs, scorecards, adoption measures, and executive value-realization reporting.
Modernize automation portfolios from UI-based RPA toward API-driven and intelligent automation.
Partner with Architecture, Engineering, Product, Data, and Cloud teams to challenge recommendations and influence future-state designs.
Lead cross-functional execution, communicate investment decisions, and manage risks, dependencies, and stakeholder alignment.