Led a company-wide initiative to protect customers and brand trust on corporate social channels by developing a machine learning model that detects and blocks scammers in real time. Combined product strategy, customer experience, and brand protection efforts in partnership with ML Engineering, Legal, Risk, and Support.

Ideation & Vision: Identified growing brand and customer risk across social platforms and defined the problem space. Developed the product vision and strategy for a machine learning solution to detect and block scammers targeting our brand and customers in real time.

Business Impact Analysis: Assessed ROI potential and long-term business impact. Evangelized the concept across the organization, presenting a clear value proposition to five ML engineering teams and aligning the initiative with customer trust and brand protection goals.

Cross-Functional Leadership: Identified an aligned engineering team and created a compelling business case to secure support from technical leadership and stakeholders.

Workshops & Planning: Facilitated cross-functional workshops with Support, Risk, and Engineering teams to co-develop the product roadmap, surface risks, prioritize features, and define key milestones.

Legal & Compliance: Collaborated with Legal and Risk partners to assess compliance, manage IP concerns, and gain formal approval for full deployment.

Project Execution: Led end-to-end project delivery with engineering teams — driving experimentation, unblocking technical issues, and keeping momentum high through roadblocks. Maintained focus on innovation, risk mitigation, and real-time performance optimization.

Results: