Principal - Data Scientist

Job Title: Principal Data Scientist Department, Level: Viewer Experiences Location: Onsite, Bangalore Work Type: Full time Job Summary: We believe personalization is the operating system of the customer experience—not just a recommendation engine. As a Principal Data Scientist, you will define the scientific vision and technical strategy for an AI-native personalization platform that powers Search, Recommendations, Homepage, Ads, and Notifications for millions of users. This is a high-impact leadership role where your work will directly influence customer engagement, retention, and Watch Time Per User (WTPU), while setting the direction for one of the industry's most advanced personalization ecosystems. If you're passionate about building AI systems at massive scale and solving complex customer problems, this is your opportunity to make a lasting impact. About the team: At JioHotstar, the Viewer Experience (VX) org is at the heart of how millions discover, engage with, and fall in love with our platform. We own the end-to-end user journey—from first app launch to daily habit loops—across Search, Personalization, Watch Experience, Interactivity, and more. We blend world-class engineering, ML, design, and data to deliver a seamless, personalized, and engaging OTT experience at massive scale. If you're passionate about building immersive, intelligent, and performant user experiences that delight a billion users, join us in shaping the future of streaming. Reporting To: Key responsibilities: Define the personalization vision: Lead the evolution of JioHotstar's AI-native personalization platform, by designing systems that continuously understand every content we carry on the platform, who the customer is, what they intend to consume, and what they are most likely to value next, enabling seamless personalization across Search, Recommendations, Homepage, Ads, and Notifications. Architect next-generation AI systems: Build scalable, real-time personalization solutions using representation learning, foundation models, sequential user modeling, multimodal intelligence, semantic retrieval, and continuously learning systems. Solve personalization at scale: Drive innovation in customer understanding, intent prediction, cold-start, real-time recommendations, exploration vs. exploitation, diversity, and low-latency decision-making Build Platforms, Not Models: Develop foundational capabilities such as unified user and content representations, experimentation frameworks, feature platforms, and real-time inference services. Drive organizational thinking beyond optimizing isolated algorithms toward building continuously learning intelligence systems. Lead scientific excellence: Partner with Product, Engineering, Analytics, and Business leaders to define long-term AI strategy