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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Lead Data Science Consultant - **Company:** Wells Fargo - **Location:** Phoenix, AZ, United States - **Experience:** Expert - **Salary:** $159,000.0 - $279,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Business Analytics Applications, Data Analysis, Big Data, Cyber Security, Information Engineering, R (Programming Language), Python (Programming Language), Machine Learning, Power BI, SAS (Software), SQL Databases, Tableau (Software), Large Language Models, Generative AI, Information Technology, Performance Monitor, Data Management, Machine Learning Operations, Alteryx - **Published:** September 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=2662a24c97299b5c ## About the Role * 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., * 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. ## Description * Lead the strategy and execution of highly complex, large-scale data, analytics, and AI initiatives that span multiple business and technology functions. * 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. ## 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