Sr. Product Manager, Machine Learning at Apple
California, United States
Ankit
Roy
Council Member
Ankit Roy is a Senior Product Manager specializing in machine learning and AI applications, with over 14 years of experience spanning product management, software engineering, and entrepreneurship. He has built and shipped ML systems at scale, led cross-functional teams, and driven measurable business outcomes across consumer products, enterprise software, and growth platforms.
Ankit Roy currently serves as Senior Product Manager, Machine Learning at Apple Inc., a role he has held since December 2019 within the Apple Services organization. In this capacity, he defined the strategy and roadmap for a suite of applied AI/ML solutions, including libraries and frameworks covering large language models, causal inference, natural language processing, deep learning, forecasting, and anomaly detection, all powering growth and engagement across Apple Services properties such as the App Store, Apple TV, and Apple Music. He led the development of an NLP and LLM-based Review Summarization library that delivered synthesized user feedback for Apple Services apps and ultimately contributed to the App Store's AI-generated review summaries, launched to hundreds of millions of users globally. He also built and shipped a scalable Anomaly Detection Framework adopted across Apple Services engineering, business, and operations teams, successfully onboarding organizations spanning Data Science, Analytics Engineering, Wallets, Payments and Commerce, iCloud, and News. Extending this work into trust and safety, he scaled the App Store Trust and Safety Program by enabling teams to apply anomaly detection and NLP capabilities to improve app rejection processes and prevent over $2 billion in fraudulent transactions. He designed and delivered a Causal Inference Library adopted by multiple Services Data Science teams to augment A/B testing, measure true feature impact, and optimize major consumer features including App Store In-App Events, Search improvements, Apple TV Redesign, Apple Music, and Apple Pay Subscriptions. He also built a marketing A/B experimentation platform and an applied-ML attribution and measurement solution that optimized Apple Services' multi-hundred-million-dollar paid media budget across paid channels. A privacy-first on-device ML initiative he led, developed in partnership with Legal and Privacy teams, is now deployed across Apple Games, Apple Music, Apple TV, and the App Store. He additionally created a multi-agent RAG and LLM application for knowledge discovery, enabling cross-functional teams to extract insights from diverse data sources and significantly reducing time-to-insight.
Earlier at Apple, Ankit Roy served as Program Manager, Business Operations within Apple Services from December 2017 to December 2019. He led a team of 20 vendor resources, delivering a high volume of analytics projects and ad hoc requests from business operations teams across the organization. He served as the single point of contact for the Legal team's analytics needs, supporting over 250 cases globally spanning product, competition, regulatory, and litigation matters across the App Store, Music, Video, and Books businesses. He provided subject matter expertise on Services business data models covering commerce, subscriptions, clickstream, and content, coordinating across cross-functional teams to help develop appropriate response strategies for high-profile cases worldwide.
Prior to joining Apple, Ankit Roy held a Product Manager role at SAP from August to December 2017, where he conducted market analysis for the GRC product suite, prioritized a roadmap to drive adoption of the newly launched SaaS model, and led engineering and design teams to build a Rule Builder AI solution for managing access rules with versioning and simulation to identify and mitigate access and segregation-of-duties risks.
Ankit Roy currently serves as Senior Product Manager, Machine Learning at Apple Inc., a role he has held since December 2019 within the Apple Services organization. In this capacity, he defined the strategy and roadmap for a suite of applied AI/ML solutions, including libraries and frameworks covering large language models, causal inference, natural language processing, deep learning, forecasting, and anomaly detection, all powering growth and engagement across Apple Services properties such as the App Store, Apple TV, and Apple Music. He led the development of an NLP and LLM-based Review Summarization library that delivered synthesized user feedback for Apple Services apps and ultimately contributed to the App Store's AI-generated review summaries, launched to hundreds of millions of users globally. He also built and shipped a scalable Anomaly Detection Framework adopted across Apple Services engineering, business, and operations teams, successfully onboarding organizations spanning Data Science, Analytics Engineering, Wallets, Payments and Commerce, iCloud, and News. Extending this work into trust and safety, he scaled the App Store Trust and Safety Program by enabling teams to apply anomaly detection and NLP capabilities to improve app rejection processes and prevent over $2 billion in fraudulent transactions. He designed and delivered a Causal Inference Library adopted by multiple Services Data Science teams to augment A/B testing, measure true feature impact, and optimize major consumer features including App Store In-App Events, Search improvements, Apple TV Redesign, Apple Music, and Apple Pay Subscriptions. He also built a marketing A/B experimentation platform and an applied-ML attribution and measurement solution that optimized Apple Services' multi-hundred-million-dollar paid media budget across paid channels. A privacy-first on-device ML initiative he led, developed in partnership with Legal and Privacy teams, is now deployed across Apple Games, Apple Music, Apple TV, and the App Store. He additionally created a multi-agent RAG and LLM application for knowledge discovery, enabling cross-functional teams to extract insights from diverse data sources and significantly reducing time-to-insight.
Earlier at Apple, Ankit Roy served as Program Manager, Business Operations within Apple Services from December 2017 to December 2019. He led a team of 20 vendor resources, delivering a high volume of analytics projects and ad hoc requests from business operations teams across the organization. He served as the single point of contact for the Legal team's analytics needs, supporting over 250 cases globally spanning product, competition, regulatory, and litigation matters across the App Store, Music, Video, and Books businesses. He provided subject matter expertise on Services business data models covering commerce, subscriptions, clickstream, and content, coordinating across cross-functional teams to help develop appropriate response strategies for high-profile cases worldwide.
Prior to joining Apple, Ankit Roy held a Product Manager role at SAP from August to December 2017, where he conducted market analysis for the GRC product suite, prioritized a roadmap to drive adoption of the newly launched SaaS model, and led engineering and design teams to build a Rule Builder AI solution for managing access rules with versioning and simulation to identify and mitigate access and segregation-of-duties risks.
During his MBA program, Ankit Roy completed a product management internship at Cisco from May to August 2016, contributing to WebEx growth initiatives. He led a redesign of WebEx admin screens, introduced partner-facing KPIs for sales guidance, and analyzed the sales funnel to identify bottlenecks in the trial signup process, simplifying onboarding steps to improve trial conversion lift. Concurrently, he served as Co-Founder and Startup Fellow at the Georgia Institute of Technology's Ti:GER Startup Incubator from January 2016 to May 2017, working to commercialize a novel patented human interaction technology for wearable devices, conducting market research, building business strategy, and leading investor pitches.
Earlier in his career, Ankit Roy spent nearly five years as a Senior Software Engineer at Infosys from August 2010 to May 2015, serving as a lead developer for iOS and macOS enterprise applications and as a member of the Infosys Mobile Center of Excellence. During this period he built a custom iOS application that analyzed iPhone sales data across regions, resellers, and carriers for Apple's sales and executive teams, and developed an iOS app for real-time defect management in Apple's third-party manufacturing plants that reduced defect resolution turnaround time by a third. He also developed iOS and macOS applications supporting supply chain planning, gift card sales automation, and hardware cost optimization by analyzing millions of data points across supplier volume and price mix.
In parallel with his MBA studies, Ankit Roy served as an Investment Associate at Atlanta Technology Angels from 2016 to 2017, conducting technical and business due diligence for seed-stage startups seeking angel investment.
Ankit Roy has published research through Apple Machine Learning Research, with papers covering the Anomaly Detection Framework and the App Store Review Summarization NLP and LLM work. His contributions to App Store Trust and Safety improvements and the Causal Inference program have been covered in the Apple Newsroom. He has presented at Apple's internal AI/ML Summit in both 2022 and 2023, events drawing over 7,500 attendees across 505 submissions. He has also been recognized in Lewis Lin's widely referenced book "The Product Manager Interview" and received recognition from Product School.
Ankit Roy holds a Master of Business Administration from the Georgia Institute of Technology, where he served as elected President of the MBA Technology Management Club, and earned his Bachelor of Engineering in Computer Science from Visvesvaraya Technological University. His technical expertise spans machine learning, large language models, natural language processing, causal inference, anomaly detection, deep learning, forecasting, product analytics, A/B experimentation, iOS and macOS development, and applied AI for consumer and enterprise products.
Earlier in his career, Ankit Roy spent nearly five years as a Senior Software Engineer at Infosys from August 2010 to May 2015, serving as a lead developer for iOS and macOS enterprise applications and as a member of the Infosys Mobile Center of Excellence. During this period he built a custom iOS application that analyzed iPhone sales data across regions, resellers, and carriers for Apple's sales and executive teams, and developed an iOS app for real-time defect management in Apple's third-party manufacturing plants that reduced defect resolution turnaround time by a third. He also developed iOS and macOS applications supporting supply chain planning, gift card sales automation, and hardware cost optimization by analyzing millions of data points across supplier volume and price mix.
In parallel with his MBA studies, Ankit Roy served as an Investment Associate at Atlanta Technology Angels from 2016 to 2017, conducting technical and business due diligence for seed-stage startups seeking angel investment.
Ankit Roy has published research through Apple Machine Learning Research, with papers covering the Anomaly Detection Framework and the App Store Review Summarization NLP and LLM work. His contributions to App Store Trust and Safety improvements and the Causal Inference program have been covered in the Apple Newsroom. He has presented at Apple's internal AI/ML Summit in both 2022 and 2023, events drawing over 7,500 attendees across 505 submissions. He has also been recognized in Lewis Lin's widely referenced book "The Product Manager Interview" and received recognition from Product School.
Ankit Roy holds a Master of Business Administration from the Georgia Institute of Technology, where he served as elected President of the MBA Technology Management Club, and earned his Bachelor of Engineering in Computer Science from Visvesvaraya Technological University. His technical expertise spans machine learning, large language models, natural language processing, causal inference, anomaly detection, deep learning, forecasting, product analytics, A/B experimentation, iOS and macOS development, and applied AI for consumer and enterprise products.