Manager - Machine Learning Engineering and Data Science

 Manager, Machine Learning Engineering & Data Science , Hyderabad

About Warner Bros. Discovery: 

Warner Bros. Discovery, a premier global media and entertainment company, offers audiences the world's most differentiated and complete portfolio of content, brands and franchises across television, film, streaming and gaming. The new company combines Warner Media’s premium entertainment, sports and news assets with Discovery's leading non-fiction and international entertainment and sports businesses. 

For more information, please visit www.wbd.com. 

Meet Our Team: 

When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons. and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next… 

From brilliant creatives to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive. 

Roles and Responsibilities: 

The Manager – Machine Learning Engineering & Data Science will lead a team of Machine Learning Engineers and Data Scientists responsible for building, deploying, and operating large‑scale ML systems that power critical business capabilities across consumer platforms. 

This role brings together Data Science, ML engineering, platform reliability, and people leadership, with the opportunity to shape production ML systems in areas such as user behavioral modeling, fraud and abuse detection, transaction intelligence, and lifecycle engagement. 

This role will also lead the development of ML platform capabilities across the full lifecycle, including pipeline orchestration, experimentation frameworks, model promotion, deployment, and observability, enabling scalable and reliable production ML systems. 

The role requires strong hands‑on technical depth, experience in designing data science experiments, architecting scalable ML solutions, and building production-grade systems, along with the ability to scale teams, processes, and platforms. 

Key Responsibilities 

• Own end-to-end delivery: Accountable for delivering ML/DS initiatives from problem definition through production, ensuring timelines, quality, and business impact are consistently met. 

• Own ML platform and lifecycle capabilities: Build and evolve core systems for ML pipelines, experimentation, model versioning/promotion, deployment, and observability, ensuring reproducibility, governance, and production reliability. 

• Be a hands-on technical leader: Stay close to the work—review designs, guide modeling approaches, participate in key technical decisions, and help unblock complex ML and data challenges.  

• Partner deeply with product and stakeholders: Work closely with Product, Engineering, and business teams to shape problem statements, prioritize work, and ensure ML solutions align to clear outcomes and KPIs.  

• Drive execution with operational excellence: Establish strong planning, tracking, and risk management practices; ensure ML systems are production-ready with robust pipelines, automated deployment workflows, and end-to-end monitoring/observability. 

• Set and enforce quality standards: Own the bar for model performance, data quality, experimentation rigor, and system reliability in production. 

• Lead and develop the team: Hire, mentor, and manage a team of MLEs and Data Scientists; provide clear goals, regular feedback, and career development to build a high-performing organization. 

• Foster collaboration and shared ownership: Create strong working relationships across teams, improve how ML integrates into broader systems, and drive alignment across cross-functional partners 

What to Bring: 

• 12–15 years of total experience with 3+ years of leading and managing ML, Data Science teams. 

• Proven experience designing and delivering machine learning-driven, large-scale distributed systems in production. 

• Experience building or operating end-to-end ML platforms, including pipeline orchestration, experimentation systems, model registry/promotion workflows, and production observability. 

• Experience translating business problems into scalable ML/AI solutions with measurable impact. 

• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and modern ML/data ecosystems. 

• Strong understanding of ML platforms, data pipelines, distributed systems, and production architecture patterns. 

• Ability to collaborate effectively across Product, Engineering, Data, and Business teams. 

• Excellent written and verbal communication skills with ability to contribute to technical design discussions and documentation. 

Preferred: 

• Experience working with global, cross-functional teams. 

• Exposure to MLOps / LLMOps frameworks, including experiment tracking, model governance, promotion strategies, deployment pipelines, and observability tooling. 

• Familiarity with modern AI patterns (e.g., GenAI, LLM-based applications, RAG, or applied AI systems). 

• Experience establishing reusable technical patterns, standards, and scalable frameworks across teams. 

What We Offer: 

• A Great place to work. 

• Equal opportunity employer 

• Fast track growth opportunities