Senior PM | Privacy, Security & AI Systems
AI/ML privacy product leader who builds secure-by-default, privacy-preserving data and measurement platforms that satisfy regulators, unblock engineering teams, and improve product quality at large-scale consumer technology companies.
Strategic product work balancing user privacy, regulatory compliance, and engineering velocity - from platform architecture to interactive product demonstrations.
Challenge: Product teams at a large-scale consumer technology company needed deeper visibility into user engagement and feature performance, but existing measurement tools created privacy risks and couldn't meet strict privacy standards or comply with evolving regulations.
Product Strategy: Led design and rollout of a privacy-preserving measurement platform using privacy-aggregated identifiers. Defined product requirements that balanced engineering team needs with privacy constraints, created governance frameworks for data access and retention, and established metrics to validate both privacy preservation and product utility.
Execution: Partnered with engineering, privacy counsel, and platform teams across multiple product organizations to design secure-by-default data pipelines. Built access control systems, established privacy review processes, and created documentation and training to enable safe adoption. Navigated regulatory requirements while maintaining product velocity.
Challenge: Traditional analytics tools expose raw personally identifiable information (PII), creating significant legal and ethical risks. Product teams need data-driven insights without compromising user privacy or violating regulations like GDPR and CCPA.
Product Strategy: Built an interactive dashboard demonstrating how differential privacy techniques can transform risky SQL queries into privacy-safe aggregations. The system uses calibrated mathematical noise to protect individual users while preserving statistical accuracy for product decisions. Includes real-time privacy budget tracking (epsilon values) to ensure compliance and prevent information leakage through repeated queries.
Key Innovation: Created a "query blocker" that automatically detects unsafe queries and offers one-click transformations to safe, aggregated alternatives. This makes differential privacy accessible to non-technical users (PMs, analysts) without requiring deep privacy engineering expertise. The UX demonstrates how privacy can be a product feature, not just a compliance checkbox.
Challenge: ML models for autonomous systems required massive amounts of high-quality labeled data across multiple sensor modalities. Quality inconsistencies, annotation pipeline bottlenecks, and lack of systematic evaluation frameworks blocked model improvement.
Product Strategy: Defined end-to-end data platform vision spanning data acquisition, human-in-the-loop annotation workflows, quality assurance, and model evaluation pipelines. Created evaluation frameworks and KPI systems that aligned data science, research, and operations teams around model quality metrics.
Execution: Partnered with data science teams to design annotation workflows and quality control processes. Built scalable data delivery systems supporting multiple perception teams. Established metrics dashboards, quality gates, and feedback loops between model performance and data collection priorities. Led cross-functional team through organizational transition.
Challenge: AI training data platform needed to scale customer success operations, improve retention, and drive expansion revenue across enterprise clients while maintaining high-quality service delivery.
Product Strategy: Defined customer success product vision combining strategic account planning, white-glove onboarding, and data-driven engagement strategies. Created frameworks for identifying expansion opportunities and measuring customer health metrics that directly informed product roadmap priorities.
Execution: Led contract negotiations, built customer onboarding playbooks, and established feedback loops between customer needs and product development. Created success metrics dashboards and early warning systems for churn risk. Drove cross-functional alignment between sales, engineering, and operations teams.
Deep expertise across AI/ML systems, privacy engineering, and cross-functional product execution.
I'm an AI/ML privacy product leader with 15+ years building secure-by-default, privacy-preserving platforms at companies serving billions of users. I focus on AI/ML measurement systems that balance regulatory compliance, user privacy, and engineering velocity.
My background spans the full spectrum from hands-on DevOps and cloud architecture to executive-level strategy and stakeholder management. I've led teams of 12+, scaled platforms to thousands of users, and driven millions in revenue impact while maintaining a relentless focus on technical excellence and user trust.
What sets me apart is the combination of deep technical understanding (security, ML systems, cloud infrastructure) with program rigor, human-centered leadership, and clear product strategy. I thrive in highly regulated, technically complex environments where product success requires navigating competing constraints and aligning diverse stakeholders around shared goals.
AI/ML privacy PM/TPM who builds secure-by-default, privacy-preserving data and measurement platforms that satisfy regulators, unblock engineering teams, and improve product quality-blending deep technical understanding with program rigor, human-centered leadership, and clear product strategy.