Director, Data Science & Applied AI

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

Who We Are…

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.

Your New Role:
 

As the Director of Data Science & Applied AI, you will drive the next wave of data-driven innovation in the Media & Entertainment industry. This is a high-impact, high-visibility role, responsible for leading the strategic direction, execution, and scaling of enterprise-wide data science and applied AI initiatives. You will partner with cross-functional teams—including Data Scientists, ML Engineers, Software Engineers, and Business Leaders—to deliver transformative Data Science & AI-powered solutions that enhance audience experiences, streamline operations, and unlock business value.

You will lead and inspire a best-in-class data science and applied AI team, bringing deep technical expertise, a strong business mindset, and the ability to solve complex, high-value problems at scale. Your role will be instrumental in advancing our mission to be a global leader in AI-driven media and entertainment.

1. Advanced Data Science & AI Solution Delivery

• Oversee the end-to-end lifecycle of data science and AI solutions—from problem framing, exploratory analysis, and modeling to deployment, monitoring, and iteration.

• Lead the development and deployment of advanced analytics, machine learning, NLP, computer vision, and generative AI models that address complex business challenges.

• Ensure the creation of robust, scalable, and production-ready AI pipelines, leveraging cloud-native, microservices-based architectures.

• Embed best practices in MLOps, model governance, and responsible AI to ensure reliability, fairness, transparency, and compliance.

• 2. Strategic Leadership & Team Development

• Build, develop, and mentor a world-class data science and applied AI team, fostering a culture of excellence, experimentation, and continuous learning.

• Define and execute the roadmap for applied AI and data science projects, ensuring alignment with organizational objectives and business impact.

• Champion data-driven decision making and thought leadership, elevating the visibility and influence of the AI team across the enterprise.

• Establish and monitor performance metrics for teams and individuals, driving accountability and high-impact delivery.

• 3. Innovation, Research & Applied AI Strategy

• Identify, evaluate, and implement emerging technologies, algorithms, and methodologies to keep the organization at the forefront of AI innovation.

• Develop and execute the applied AI strategy in close partnership with executive leadership, influencing product and business roadmaps.

• Spearhead pilot programs and research initiatives in generative AI, large language models (LLMs), reinforcement learning, and other advanced fields.

• Drive thought leadership and represent the organization at industry events, conferences, and in AI communities.

• 4. Cross-Functional Collaboration & Stakeholder Management

• Partner with product management, engineering, and business teams to translate complex business problems into technical Data Science/ AI solutions.

• Collaborate on the integration of ML models into products and workflows, ensuring smooth end-to-end delivery from prototype to production.

• Act as a trusted advisor to executives and stakeholders on ML capabilities, project status, risks, and business impact.

• Drive the development and implementation of data governance, privacy, and security practices to ensure compliance with regulatory requirements.

• Facilitate the sharing of ML insights with broader company teams, providing transparency and fostering a data-driven culture.

• 5. Performance Monitoring & Reporting

• Define and track key performance indicators (KPIs) to measure the success of AI/ ML initiatives and mode