Manager, Machine Learning and Data Science
CrunchyrollHyderabad, Telangana, IndiaPosted 11 April 2026
Tech Stack
Job Description
About Crunchyroll
Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We super-serve over 100 million anime and manga fans across 200+ countries and territories, and help them connect with the stories and characters they crave. Whether that experience is online or in-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.
Join our team, and help us shape the future of anime!
About the role
We are looking for a Manager, Machine Learning and Data Science in the Hyderabad Area to lead and scale a team applying advanced Data Science and Machine Learning to a global digital entertainment platform. Our ecosystem spans paid VOD streaming , online Manga reading , Merchandising , Gaming , and Music Video experiences , all centered on deep user engagement and premium content consumption regarding the anime fandom.
In this role, you will manage a team of Machine Learning and AI Engineers, and Data Scientists focused on understanding content, user behavior, and product engagement , and turning those insights into actionable business insights and product features that offer personalized user experiences . You will partner closely with the Director of Data Science Machine Learning (DSML) to scale both the team and the technical capabilities needed to support a growing, multi-product platform.
This role is ideal for a leader who combines strong technical judgment with people leadership, and who can guide teams from exploratory analysis to production-ready ML systems that directly impact customer experience and business outcomes.
Core Areas of Responsibility
Lead, mentor, and develop a team of Machine Learning Engineers and Data Scientists, fostering a culture of technical excellence and accountability.
Drive delivery of ML and analytics initiatives that enhance content discovery, personalization, user engagement, and cross-platform experiences.
Provide technical oversight on modeling approaches, experimentation, and production readiness to ensure scalable and reliable solutions.
Translate business objectives into clear ML roadmaps in partnership with Product, Engineering, and business stakeholders.
Ensure insights and model outputs are effectively communicated to inform strategic and operational decisions.
Establish and standardize best practices across experimentation, evaluation, deployment, and monitoring of ML systems.
Identify opportunities to leverage data and ML across streaming, manga, gaming, merchandising, and music verticals.
Partner with the Director of DSML to scale team capabilities, processes, and long-term technical strategy.
About You
Required Qualifications
5+ years of experience in Data Science, Machine Learning, or Applied Analytics
2+ years of experience leading or managing technical teams
Strong understanding of ML systems applied to user behavior, content, or product analytics
Experience delivering ML solutions that inform business decisions and/or power user-facing features
Ability to guide technical design and evaluate trade-offs without being hands-on day-to-day
Strong communication skills and comfort working with product and business stakeholders
Preferred Qualifications
Experience in subscription-based digital products, media, streaming, gaming, or e-commerce
Experience with personalization, recommendations, or engagement optimization
Familiarity with ML platforms, data pipelines, and production ML workflows at scale
Experience operating in a multi-product or platform environment and cross-timezone collaborations.
What Success Looks Like
The team consistently delivers ML-driven insights and experiences that improve user engagement and satisfaction
Personalized content and service offerings become more effective, measurable, and scalable
About the Team
You will join a fast growing team of Data Scientists, Machine Learning Engineers, and AI Engineers, united by a passio ... (truncated, view full listing at source)
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