Principal Data Scientist

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 a Principal Data Scientist, you will play a pivotal role in architecting, developing, and deploying high-impact machine learning and data science solutions across Warner Bros. Discovery’s global businesses. This role is ideal for a seasoned technical leader with 14–16 years of total experience, including 10–12 years of deep, hands-on experience in applied data science, machine learning, and statistical modeling. You will translate complex business problems into scalable analytical solutions—leveraging predictive modeling, optimization, experimentation, NLP, computer vision, and modern ML engineering practices. You will lead end-to-end development of ML models, champion scientific rigor, and ensure robust operationalization through modern MLOps practices. Experience in the Media & Entertainment sector—content, streaming, audience behavior, ad intelligence, or metadata systems—is a significant plus. This is a high-impact role requiring strong technical expertise, business acumen, problem-solving skills, and the ability to guide cross functional partners across Product, Engineering, Technology, and Business domains.

1. Core Data Science & Machine Learning Expertise

• Design, build, and scale advanced machine learning models across predictive analytics, NLP, CV, forecasting, optimization, and recommender systems.

• Apply strong statistical foundations—hypothesis testing, probability modeling, causal inference, and experiment design—to solve complex business problems. • Develop interpretable and explainable ML models, ensuring scientific rigor, reproducibility, and operational robustness.