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About This Role
About this role:
Wells Fargo is seeking a Senior Lead Data Management Analyst to lead strategic data and AI enablement initiatives within Wealth and Investment Management (WIM). In this highly visible individual contributor role, you will help transform business priorities and artificial intelligence (AI) use cases into governed, scalable data solutions that support analytics, automation, intelligent decision-making, and business growth.
Reporting to the WIM Strategic Data & AI Enablement Director, you will serve as a strategic bridge between business stakeholders, data and technology organizations, helping define how enterprise data, metadata, semantic models, and AI-ready capabilities are designed, governed, and adopted. You will partner with business leaders, data product teams, architects, engineers, and governance organizations to shape data strategy, improve data accessibility, and enable trusted AI solutions across the enterprise.
This role focuses on data strategy, business analysis, data architecture direction, governance, semantic enablement, and adoption rather than hands-on application development or engineering.
In this role, you will:
- Translate business priorities and AI use cases into scalable data products, trusted data assets, semantic models, APIs, and reusable enterprise capabilities.
- Identify opportunities to improve data quality, metadata, lineage, user experience, interoperability, and adoption across existing data products.
- Define business, data, governance, quality, and control requirements that support successful data-product design and delivery.
- Partner with Data Product Designers and delivery teams to prioritize enhancements that increase business value, usability, and reuse.
- Promote enterprise data strategy and support the adoption of governed data solutions across stakeholder groups.
- Translate business requirements into data requirements, mappings, transformations, business rules, and consumption specifications.
- Analyze source systems, data flows, metadata, and data quality through stakeholder engagement, research, profiling, and hands-on SQL analysis.
- Evaluate authoritative data sources based on completeness, quality, timeliness, accessibility, lineage, governance controls, and business value.
- Recommend data integration, data provisioning, and data architecture approaches that simplify and strengthen the enterprise data ecosystem.
- Use profiling results, trends, exceptions, and findings to identify risks, improve requirements, and support decision-making.
- Assess organizational readiness for AI initiatives, including metadata maturity, data quality, provenance, governance controls, privacy, access management, and monitoring capabilities.
- Define data, semantic, retrieval, grounding, traceability, and governance requirements that support responsible AI solutions and intelligent agents.
- Design and guide semantic frameworks including ontologies, knowledge representations, metadata standards, and business vocabularies.
- Identify AI data risks, limitations, assumptions, and governance requirements and facilitate appropriate stakeholder engagement.
- Leverage AI evaluation results to improve metadata quality, semantic design, source selection, and overall data readiness.
- Identify and escalate data management, governance, semantic modeling, and AI-related risks.
- Establish end-to-end lineage and traceability from source systems through data products, APIs, semantic layers, and business use cases.
- Embed data ownership, privacy, security, permissible use, access management, quality monitoring, and change controls into solution recommendations.
- Partner with risk, compliance, privacy, information security, governance, and architecture teams to support policy alignment and regulatory expectations.
- Promote trusted, transparent, and well-governed data practices across the organization.
Required Qualifications:
- 7+ years of Data Management, Business Analysis, Analytics, or Project Management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 5+ years of hands-on experience translating business needs into data requirements, source decisions, mappings, analytical findings, or governed data solutions
Desired Qualifications:
- Experience in financial services, preferably wealth management, brokerage, trust, fiduciary services, investment management, or regulatory reporting.
- Experience supporting enterprise data, analytics, business intelligence, digital transformation, or AI initiatives within complex organizations.
- Experience leading data strategy, metadata management, data governance, semantic enablement, enterprise architecture, or data product initiatives.
- Experience with ontology management, knowledge graphs, semantic technologies, or industry standards such as RDF, OWL, SHACL, SPARQL, JSON-LD, Protégé, or FIBO.
- Knowledge of enterprise metadata, lineage, data quality, privacy, security, and governance frameworks.
- Experience profiling data, evaluating fit-for-purpose data sources, and recommending scalable data provisioning solutions.
- Understanding of data architecture, data modeling, APIs, semantic layers, virtualization, business intelligence platforms, and governed data products.
- Experience working with modern cloud-based data platforms, enterprise data catalogs, metadata solutions, or governance technologies.
- Experience applying Generative AI, Retrieval Augmented Generation (RAG), agentic AI, or related technologies to improve business processes, data discovery, governance, or productivity.
- Ability to influence senior leaders, drive alignment across organizations, and navigate ambiguity in complex environments.
- Experience mentoring teams in data governance, metadata management, semantic modeling, ontology development, or knowledge graph practices.
- Strong communication skills with the ability to translate complex technical concepts into actionable business insights.
Job Expectations:
- Hybrid Work Schedule
- This position is not eligible for Visa sponsorship.
- Travel up to 10%
Posting End Date:
5 Oct 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wells Fargo AI Hiring
Wells Fargo has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Wilmington, CA, US, Charlotte, NC, US, San Francisco, CA, US. Compensation range: $254K - $300K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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