Mgr II, Fraud Analytics

InComm Payments•Atlanta, GA

About The Position

As a Senior Manager – Fraud Strategies and Data Science on the Fraud Prevention Analytics and Machine Learning team, you will play a critical hands-on and people leadership role in designing, building, and scaling advanced AI/ML solutions to combat fraud across gift cards, prepaid cards, cash reload products, and healthcare cards. You will lead and develop a team of data scientists while applying deep analytical thinking and modern machine learning techniques to solve complex fraud challenges across both U.S. and international markets, operating at transaction-level scale. In this role, you will work end-to-end from problem formulation and data exploration to model development, deployment, and performance monitoring, while setting technical direction, prioritizing work, and ensuring high-quality execution across the team. You will drive measurable reductions in fraud while balancing customer experience and operational efficiency, and partner closely with fraud tech, product, engineering, and operations teams to translate business problems into scalable, data-driven solutions. You will regularly engage with stakeholders to communicate fraud trends, model insights, and the impact of analytical initiatives, while coaching and mentoring team members to strengthen technical skills, analytical rigor, and business acumen. Your ability to clearly explain complex models and results to both technical and non-technical audiences will be essential. This role is ideal for a leader who thrives in ambiguous problem spaces, has a strong bias toward execution, and is passionate about developing people while applying AI/ML to real-world fraud prevention problems.

Requirements

Strong understanding of fraud prevention technologies, advanced analytics, and machine learning applications in production environments.
Proven experience in an analytical or data science role (4+ years), with demonstrated success delivering scalable, high-impact ML solutions.
Demonstrated people leadership experience, including mentoring, coaching, and developing data scientists or analysts in a team setting.
Ability to set technical direction, prioritize work, and balance hands-on execution with leadership responsibilities.
Strong analytical mindset with the ability to work with large, complex datasets and apply statistical and machine learning techniques to real-world business problems.
Excellent communication and interpersonal skills, with the ability to influence stakeholders and explain complex concepts to diverse audiences.
Proven track record of driving strategic initiatives using data-driven insights while collaborating effectively across cross-functional teams.
Proactive approach to staying current on fraud trends, emerging technologies, and best practices in analytics and people leadership.

Responsibilities

Design, build, and deploy machine learning models for fraud detection and prevention, including supervised, unsupervised, and semi-supervised approaches.
Lead the technical direction for fraud ML solutions, ensuring scalable, reliable performance across high‑volume, real‑time and batch transaction data spanning multiple payment products and geographies.
Establish best practices for model development, experimentation, monitoring, and continuous improvement, and ensure consistent adoption across the team.
Provide technical guidance and code reviews to ensure high-quality, maintainable, and well-governed analytical solutions.
Drive complex analytical investigations to identify emerging fraud patterns, vulnerabilities, and attack vectors across products and markets.
Coach and mentor data scientists and analysts in applying statistical rigor, domain expertise, and creative problem-solving to ambiguous fraud challenges.
Translate complex analytical findings into actionable insights that influence fraud strategy, operational decisions, and executive-level discussions.
Lead, mentor, and develop a team of data scientists, fostering a culture of accountability, collaboration, innovation, and continuous learning.
Support hiring, onboarding, and performance development of team members, including goal setting, feedback, and career progression.
Balance hands-on technical execution with delegation and prioritization to ensure timely and high-impact delivery across initiatives.
Act as a senior thought partner to fraud technologies, product, engineering, operations, legal, and compliance stakeholders to align analytics solutions with business objectives, risk appetite, and regulatory requirements.
Partner with vendor and engineering teams to productionize models and analytics into real-time fraud systems at scale.
Communicate clearly and effectively with both technical and non-technical audiences, influencing decisions through data-driven recommendations and measurable business impact.
Stay current with advancements in machine learning, AI, and fraud detection techniques, and guide the team in applying them pragmatically to real-world problems.
Lead proof-of-concept initiatives, tool evaluations, and model experimentation efforts.
Influence and uphold best practices in model validation, governance, and responsible AI within the fraud analytics organization.