Senior Analytics Manager - Global Employee Fraud Monitoring Detection Analytics

About this role:
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.
In this role, you will:

• 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

Required Qualifications:

• 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

Desired Qualifications:

• 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

Job Expectations:

• Must be willing to work from one of the posted locations
• This position offers a hybrid work schedule
• Visa sponsorship is not available for this position

Location:

• 2850 S Price Road - Chandler, AZ
• 1525 W WT Harris Blvd - Charlotte, NC
• 2200 Concord Pike - Wilmington, DE

Posting End Date:
2 Apr 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.
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