Ofertas › Discord
Discord · San Francisco Bay Area
Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games. As Group Product Manager for Data/ML, you'll drive the vision, strategy, and roadmap for Discord's Machine Learning Platform and its applications, particularly to Advertising, while also championing how the broader company uses data to make product decisions. This is a horizontal role reporting to the VP of Product, Business that touches nearly every product team at Discord, as well as core technical teams, so you'll need to be comfortable driving alignment across stakeholders whose goals are sometimes in tension. What you'll be doing Guide the product philosophy, vision, and roadmap for Discord's ML Platform and its key use cases (Personalization, Notifications, Safety, Advertising, Recommendations). Own performance against key data and ML business metrics, and proactively report progress to stakeholders across the company. Collaborate deeply with Applied ML, ML Platform, and Data Science to build and prioritize the ML roadmap for our Advertising products. Advise product teams across Discord on how to build and instrument their products with data-first capabilities from the start. Drive cross-functional execution across all product areas, working with engineers/MLEs, security, legal, and policy partners as needed. What you should have 8+ years of combined experience in data science and product management, including direct, hands-on experience as a data scientist or in a similarly quantitative role before moving into product. 3-5+ years of product management experience owning algorithmic, data-driven systems (e.g., bidding, ranking, personalization, and/or recommendations systems), including responsibility for prioritization, trade-offs, and timelines. Proven ability to analyze and synthesize data directly, not just interpret it secondhand, including comfort with the statistical and quantitative fundamentals behind modern ML systems. Demonstrated technical proficiency working closely with ML/data engineers, including familiarity with ML fundamentals, enough to cut through the buzz and separate what's practical at scale from what isn't. Experience oper