Head of CDO AI Strategy, Enablement and Delivery
Wells Fargo is hiring a Head of CDO AI Strategy, Enablement and Delivery. Level rates it ; you can apply on Wells Fargo.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions LangChain, LangGraph and PyTorch among its tools and skills.
- Level classes it as an executive role.
- Wells Fargo has 1,400+ other open roles on Level.
- Posted in the last few days.
AI in this role
Wells Fargo is seeking a Head of AI Strategy, Enablement and Delivery for the Chief Data Office (CDO).
Responsibilities include:
• CDO AI Strategy: Define, publish, and continually refine the multi-year AI strategy and roadmap for the Chief Data Office, aligned to enterprise technology strategy, enterprise AI strategy, the Enterprise Data Strategy, and regulatory expectations.
• AI Enablement and Adoption: Build the enablement layer that allows lines of businesses to adopt AI on governed enterprise data, including reusable patterns, reference architectures, shared services, evaluation harnesses, and self-service tooling.
• AI Education and Adoption Series: Empower and equip CDO resources through an ongoing AI educational series that builds skills and confidence, improving adoption of AI tools across the data organization.
• Business Line Engagement: Engage executive sponsors, business unit leaders, and technology partners to identify and build high-impact AI opportunities, build consensus for investment, and drive literacy and change management that make adoption stick. Represents the Chief Data Office in enterprise investment and governance forums and builds consensus across peers without direct authority.
• Responsible AI: Align to enterprise standards for responsible AI, including model governance, bias mitigation, explainability, transparency, human oversight, and compliance with applicable regulatory requirements.
• Risk and Regulatory Interface: Partner with Model Risk Management, Technology Risk, Compliance, Legal, Privacy, and Cybersecurity so that AI solutions are designed for control effectiveness and audit sustainability; Serves as the accountable leader for AI within CDO, including control design, issue remediation, and regulatory and audit engagement. Serves as the primary liaison to regulatory bodies (e.g., OCC, FRB) and to internal audit on AI matters and chairs the Chief Data Office AI forum with risk and governance partners.
• AI Risk Policy Adherence: Ensure adherence to Technology, Data, and Model Risk policies for all AI-developed use cases, from design through deployment and ongoing monitoring.
• Delivery Excellence: Oversee end-to-end execution of AI initiatives from ideation through production, partnering with the aligned technology team to set priorities and ensure successful technology capability development and deployment. Holds direct accountability for the availability, resilience, performance, and unit cost of the enterprise AI services owned by the Chief Data Office, including shared services provided to CDO and Data Domains, operating to defined service-level objectives.
• Platform Partnership: Partner with data platform, infrastructure, and technology operations teams so that AI workloads are supported by governed data, appropriate compute, and repeatable deployment and monitoring practices.
• Agentic AI Solutions: Drive the delivery of agentic AI solutions to govern enterprise data at scale, automating metadata, lineage, classification, data quality detection, and policy compliance.
• Measurement and Reporting: Define the metrics, KPIs, and measurement frameworks that quantify AI business impact in revenue growth, cost reduction, and risk mitigation, and report results to the Chief Data Officer and senior governance committees.
• Vendor and Financial Stewardship: Own the financial plan for the function, including approval of build, buy and partner decisions within a delegated authority, management of headcount, vendor spend, and consumption-based technology cost, and management of strategic partner relationships with performance and risk discipline.
• AI Tools and Token Cost Management: Own the inventory and management of AI tools for the Chief Data Office, and effectively manage token consumption and cost for the CDO division, including coordinating token forecasts with CXO partners.
• Talent and Leadership: Build and lead a multi-layer organization of AI product managers, and enablement specialists; mentor emerging leaders and foster a culture of accountability, experimentation, and excellence. Lead multiple sub-functions and geographies, including strategic vendor teams, and own the organizational design, location strategy, and succession plan for the function.
Alignment to the Enterprise Data Strategy
This role supports all three pillars of the Enterprise Data Strategy, making AI a force multiplier within each:
• Comprehensive Enterprise Data Governance: Apply AI and agentic solutions to govern data at scale, and ensure that AI data is itself governed, with models, prompts, agents, and training data inventoried, owned, monitored, and evidenced to regulator-ready standards.
• Data Strategy and Simplified Data Environments: Use AI to accelerate simplification through automated code and pipeline conversion, schema mapping, redundancy detection, and rationalization analysis, and standardize on enterprise AI patterns and platforms designed for reuse.
• Enhanced Analytic Capabilities: Extend analytics from descriptive reporting to predictive, generative, and agentic capability delivered on trusted data, equipping analytics and business teams to build safely within enterprise guardrails.
Critical Leadership Capabilities
• Strategic Thinking: Anticipates where AI capability and regulatory expectation are heading and positions the enterprise ahead of both; translates strategy into specific objectives and key results for units and individuals; provides board-level communication of efforts and progress.
• Driving Results: Delivery-oriented with a concentrated focus on quality for cornerstone capabilities; reviews performance and progress regularly, tests whether goals are sufficiently challenging, and implements corrective action based on deviations.
• Collaborating and Influencing: Strategically engages stakeholders across Technology, Risk, Compliance, Legal, and the lines of business to shape end-state designs; comfortable in front of large audiences, presenting a message clearly and succinctly to business, product, and executive leaders.
• Building People Capability: Serves as a talent magnet for the technology organization; grows followership of high-caliber talent, gives critical feedback in behavioral terms, and uses a regular assessment process to focus development effort.
Required Qualifications
• 10+ years of Technology Strategic Leadership experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 4+ years of Management experience, including experience leading other people leaders.
• 8+ years of experience in data, analytics, or AI roles, including data management or data engineering.
• 5+ years of experience owning and driving roadmap strategy and definition for technology products or platforms.
• 4+ years of Risk Management, Business Controls, or Compliance experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
Desired Qualifications
• Demonstrated track record developing and executing AI or advanced analytics strategy at enterprise scale, including production deployment in cloud environments.
• Executive leadership experience in large, highly regulated financial services organizations, including regulatory and audit engagement.
• Experience leading the AI charter for a large technology or financial technology organization.
• Hands-on familiarity with retrieval-augmented generation, agentic workflows, and generative AI application patterns, and with frameworks such as PyTorch, TensorFlow, LangChain, and LangGraph.
• Understanding of MLOps practices, model monitoring, and performance and cost tuning for AI workloads.
• Deep knowledge of AI governance and ethics, model risk management, privacy, and emerging AI regulation.
• Experience with data governance disciplines including metadata, lineage, data quality, and issue remediation.
• Experience operating in product-centric and agile delivery models, and with organizational design in large matrixed organizations.
• Proven ability to build and drive adoption of new tools and capabilities, and to lead globally distributed teams and strategic vendor partnerships.
• Advanced degree in Computer Science, Data Science, Engineering, Business, or a related field.
Job Expectations:
- Ability to travel up to 15% of the time.
- Ability to work on-site in one of the listed locations in a hybrid model. There is no option for fully remote work.
Posting End Date:
6 Nov 2026*Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
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.
How we rate this
Head of CDO AI Strategy, Enablement and Delivery at Wells Fargo rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Walk me through how you've used LangChain in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, ML Ops, AI Safety, and LangChain. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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