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Wells Fargo is seeking a Senior Lead Analytics Consultant to join the Global Employee Fraud Monitoring Detection Analytics (GEFMDA) team. This role is responsible for leading high‑impact fraud analytics, monitoring, and response initiatives across the enterprise. The position partners closely with executives, senior stakeholders, and cross‑functional risk teams to assess internal fraud events, identify emerging risk patterns, and design proactive, data‑driven monitoring solutions. This role operates in a highly complex, fast‑moving environment and requires deep internal fraud domain expertise, advanced analytical judgment, and strong executive communication skills. The successful candidate will deliver analytically rigorous, credible, and defensible solutions that withstand executive, regulatory, and audit scrutiny.
Job Responsibility:
Provide deep technical expertise across enterprise data sets, fraud rules, and analytical methodologies to design, enhance, and govern internal fraud monitoring strategies
Leverage SAS and SQL to extract, analyze, and interpret large, complex enterprise data sets to support internal fraud detection, monitoring, and reporting
Partner with senior stakeholders and cross‑functional teams to lead enterprise‑level, high‑priority initiatives with significant risk impact
Translate complex business, risk, and control requirements into innovative analytical solutions that materially reduce internal fraud risk
Serve as a trusted advisor to senior leadership, driving alignment and consensus to ensure solutions are credible, defensible, and aligned with enterprise risk objectives
Deliver analytics and monitoring solutions that withstand significant executive, regulatory, and audit scrutiny
Work independently with Internal Risk Management (IRM), Control, Audit, and other cross‑functional partners to provide thought leadership on fraud risk, monitoring strategies, and control enhancements
Act as a subject matter expert, influencing partners through insights, recommendations, and consultative guidance
Navigate ambiguity and evolving requirements while maintaining high standards for analytical rigor, governance, and documentation
Requirements:
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
5+ years of hands‑on experience with SAS and SQL, including querying and analyzing large enterprise data sets to support fraud analytics, monitoring, and reporting
Nice to have:
Deep knowledge of internal fraud risk management, employee fraud typologies, and proactive monitoring strategies
Experience with advanced analytics techniques, including statistical modeling, machine learning, and decision‑tree‑based methodologies
Educational background in a quantitative or analytical discipline (e.g., Statistics, Economics, Mathematics) with several years of hands‑on experience performing advanced data analytics in a professional environment
Proven ability to partner with senior executives and cross‑functional stakeholders, build alignment, and influence outcomes in high‑visibility settings
Strong executive‑level communication skills, with the ability to clearly articulate complex analytical insights, risks, and recommendations to leadership, audit, and regulatory audiences
Demonstrated success operating in high‑scrutiny environments, ensuring solutions are credible, defensible, and aligned with enterprise standard
Proven track record of delivering analytics and monitoring solutions that withstand executive, audit, and regulatory review while achieving measurable risk‑reduction outcomes
Experience working independently in fast‑paced, ambiguous environments with evolving requirements and high expectations
Background leveraging data, analytics, and fraud rules to design, enhance, or govern proactive monitoring and detection strategies
Experience with Python and familiarity with additional analytics tools and platforms is desired to accelerate analytical development and automation