Manager, Data Science and Machine Learning

Crunchyroll
Hyderabad, Telangana, IndiaPosted 24 February 2026

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 Crunchyroll is growing and changing, presenting unique challenges and opportunities to support millions of anime fans around the world. The AI/ML team provides seamless help to our internal stakeholders, ensuring an exceptional experience for all Crunchyroll fans. Lead and mentor a team of data scientists focused on developing and deploying ML solutions including GNNs, LLM-based applications, and fraud detection models. Define and drive the roadmap for AI/ML initiatives across Crunchyroll’s product and platform ecosystem. Guide the development of robust ML pipelines and infrastructure, working closely with data engineering, software engineering, and DevOps teams. Establish best practices for experimentation, model evaluation, A/B testing, and continuous learning in production environments. Work with stakeholders across business, marketing, security, and content teams to identify AI opportunities and translate them into actionable projects. Ensure responsible AI/ML development by emphasizing model interpretability, performance, and fairness. Own operational metrics around model impact, latency, cost, and system scalability. Foster a strong team culture centered on innovation, curiosity, collaboration, and continuous learning. About You We get excited about candidates, like you, because... Bachelor's or Master's in Computer Science, Statistics, Data Science, or a related quantitative field. 5+ years of industry experience in data science or applied ML, with 2+ years of people leadership experience. Proven track record of delivering production ML systems that solve real-world business problems. Deep knowledge of ML/AI frameworks (e.g., PyTorch, TensorFlow, HuggingFace) and experience with applied NLP, GNNs, GraphRAG, and embeddings. Experience building systems leveraging Graph Databases, Vector Stores (e.g., FAISS, Pinecone), and cloud-native ML stacks (SageMaker, Databricks, etc.). Strong technical leadership, with the ability to direct teams to develop optimal ML solutions and guide research-to-production lifecycle. Excellent communication and stakeholder management skills; able to translate between business and technical worlds. A passion for building high-performing teams, mentoring others, and fostering a learning-first environment. About the Team The RD team is dedicated to developing, testing, and validating robust and scalable machine learning models that drive business objectives. Our focus includes enhancing operational processes through AI/ML solutions, such as trend analysis, anomaly detection, and the deployment of large language models (LLMs) for tasks like querying system health. Another major focus area is preserving, and improving customer experience and retention. We closely work with our stakeholders to ensure AI/ML objectives are clearly defined. About our Values We want to be everything for someone rather than something for everyone and we do this by living and modeling our values in all that we do. We value Courage. We believe that when we overcome fear, we enable our best selves. Curiosity. We are curious, which is the gateway to empathy, inclusion, and understanding. Kaizen. We have a growth mindset committed to constant forward progress. Service. We serve our community with humility, enabling joy and belonging for others. Our commitment to diversity and inclusion Our mission of helping people belong reflects our commitment to diversity inclusion. It's just the way we do business. We ... (truncated, view full listing at source)
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