AMP Technical Fraud Analyst Manager - Strategic Data Solutions

About the role

The Strategic Data Solutions (SDS) team is looking for an experienced, results-oriented Analytics Manager to lead a team focused on combating fraud, waste and abuse. The manager guides Technical Fraud Analysts in building and deploying analytical and tactical solutions that detect, prevent and mitigate fraud across multiple programs, while fostering data-driven decision-making, career development and continuous improvement.

Key responsibilities

• Lead the team in developing analytical tools and automations that meet functional needs
• Collaborate with and support the team to innovate, encouraging experimentation and growth
• Mentor the team to develop their technical and professional skills, balancing individual wants and business needs
• Drive personal innovation projects to improve the team's and/or program's efficiency
• Functionally manage Technical Fraud Analyst (TFA) work
• Develop and implement TFA KPIs, drive prioritization and ensure metrics align with program targets
• Project manage team initiatives as needed
• Work with internal and external partners to understand program needs and create solutions for combined challenges

Required profile

• Bachelor's degree in Computer Science, Statistics, Applied Math, Engineering, a related field, or relevant experience
• 7+ years of hands-on experience in data science, analytics engineering or similar technical analytics roles
• 2+ years of experience leading and mentoring data-focused teams
• Strong analytical background applying data science techniques such as experimental design and statistical analysis
• Experience with large-scale data pipelines and production-level analytics platforms
• Proven ability to drive measurable business impact through data and automation, and to connect insights to business outcomes

Required skills

• Proficiency in SQL and at least one programming language (e.g., Python, R)
• Preferred: data storytelling and stakeholder-ready visualizations
• Preferred: familiarity with modern data platforms (Spark, Snowflake, Airflow) and ML ops tooling
• Preferred: ability to switch between strategic and tactical situations and to explain complex technical concepts in business terms