Responsible for a team of data/research scientists and analytics engineers with a focus on improving platform safety while maintaining operational efficiency. The team specializes in automating proactive detection for time-sensitive events, case prioritization, surfacing bad actors, and enabling tooling and data curation for new product policy.

Led Spotify-wide efforts to design, develop, and implement methods, processes, systems, and models to translate fairness research at scale in production-ready systems. My research in this role primarily focused on creating reproducible evaluation methodologies for machine learning teams to measure potential harm in their recommendation systems.

Founded Spotify’s ML Engagement team with a mission to democratize and improve the state of ML across Spotify. With that team, I helped over 50 different teams across Spotify understand ML best practices, productionize ML workflows and implement impactful ML in their products.