About

Andrew Yaroshevsky is a seasoned product and engineering leader with 18 years of experience across four countries. He has worked with consumer-facing and B2B SaaS products and has a strong focus on building machine-learned products. Andrew is currently the Director of Product Management at Google, where he stays laser-focused on AI/ML and works on ranking and personalization of Google Discover. Andrew is a Y Combinator alumni and has held leadership positions at Apple and Amazon. At Amazon, he led a team of ML engineers as a product and engineering manager in Search Science & AI (A9) and championed a new program for improving search quality. He influenced nearly a thousand engineers across the company and created a new human-annotated relevance metric used to validate every experiment, benchmark with competitors, and report to SVPs. The new mechanism covered over 200 experiments to improve on this metric in a year, translating into over $xB of additional revenue. Andrew is a visionary leader who crafts a product vision that brings a sense of purpose and partners with teams to align it with stakeholders, executives, and high-profile research scientists. He earns the trust of many bright people by diving deep into product logs (Python, SQL, Spark, Hadoop, Pandas, Seaborn), interviewing customers, defining north-star metrics, building strong engineering teams, and facilitating collaboration. Andrew attracts talent, builds teams, mentors and grows leaders. He has been leading engineers and managers in every company he worked at in the past 15 years. Andrew holds a Master's degree in Computer Science from the National Metallurgical Academy of Ukraine and a Bachelor's degree in Computer Science from Paton College of Welding and Electronics. He also completed the W2016 program at Y Combinator. Andrew's tech stack includes expertise in product management, AI, ML, SQL, Seaborn, Spark, Search, Python, Research Scientist, Pandas, Hadoop, and Senior Software Engineer. With 17.53 years of relevant experience, Andrew is a valuable asset to any organization looking to build and scale machine-learned products.