Senior Lead Data Scientist - AI for Data Management

About this role:

Wells Fargo is seeking Senior Lead Data Scientist role as part of the Consumer Model Development CoE. AI is fundamentally transforming data management, redefining how organizations handle metadata, data lineage, data quality, and governance at scale. The ability to harness AI, particularly Generative AI (GenAI), is unlocking unprecedented efficiencies in data discovery, cataloging, compliance, and automation—allowing banks to manage vast and complex data ecosystems with intelligence and precision. 

You will be a senior individual contributor (IC) leader responsible for driving AI-powered transformation within data management for Consumer Banking. Work closely with data management leadership to define priorities, develop an actionable AI roadmap, and lead cross-functional teams to deliver measurable results. Leverage GenAI and advanced AI techniques to solve practical challenges in data governance, metadata management, lineage tracking, data quality, and compliance.

This role requires both strategic vision and hands-on expertise—the ability to define an AI-driven strategy for data management while being deeply involved in execution as needed to drive real-world impact.

Learn more about the career areas and lines of business at wellsfargojobs.com.

In this role, you will:

• Serve as a senior AI leader driving AI/GenAI-powered innovation for data management in consumer banking.

• Partner closely with data management leadership to define strategic AI priorities and translate them into tangible workstreams.

• Develop and implement GenAI and AI-driven solutions for metadata management, data lineage, data discovery, data quality, governance, regulatory compliance, and automation.

• Lead cross-functional teams through planning, prioritization, and execution of AI initiatives, ensuring alignment with business objectives and compliance needs.

• Provide technical leadership on GenAI and AI technology adoption, ensuring the organization leverages cutting-edge innovations.

• Ensure AI-driven solutions comply with banking regulations and governance frameworks, proactively addressing risk and compliance concerns.

• Advocate for AI adoption in data management, driving cultural and technological transformation.

• Effectively communicate strategic insights and program-level updates to C-level executives and stakeholders.

Required Qualifications:  

• 7+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

• Demonstrated expertise in Generative AI, including large language models, Generative AI frameworks, and advanced NLP techniques which includes leading GenAI projects from conception to production 

Desired Qualifications:

• Extensive knowledge of data management: including but not limited to metadata management (data dictionaries, catalogs), data lineage tracking, governance frameworks, data quality management, data discovery, and regulatory compliance.

• Expert-level AI and GenAI expertise: deep experience in designing, developing, and applying LLMs, NLP, knowledge graphs, embeddings, and ML-driven automation to data management challenges.

• Strong hands-on programming skills: Proficiency in Python, SQL, cloud-based AI/ML services (AWS, GCP, Azure), vector databases, retrieval-augmented generation (RAG), and model fine-tuning.

• Ability to “zoom out and zoom in”: capable of shaping AI strategy for data management while being hands-on in guiding AI modeling and system architecture.

• 7+ years of experience in data management with a strong understanding of banking regulations, compliance requirements, and AI-driven transformation initiatives.

• Cross-functional leadership: ability to lead multidisciplinary teams, prioritize AI workstreams, and drive execution through coaching and strategic planning.

• Strong bias for action: ability to move qu