Principal Data Scientist

Storytelling is, and will always be, the heart of what we do at Skydance. Our vision is to build a thriving entertainment ecosystem where stories and worlds are imagined, brought to life and experienced. We create for every age and audience, anchored by the strength of our content and experiences and the depth of our connection with fans. We are creator-first, audience-focused, tech-forward, globally scaled. Here, you’ll work with talented teams that move fast, think boldly and collaborate with purpose to deliver results. We aim high, champion the best ideas and win together, earning the trust of our employees, creators, partners and audiences every day. If you’re energized by the opportunity to help build the next-generation global media and entertainment company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO! ㅤ 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. Conduct feature engineering, model selection, hyperparameter tuning, and validation using modern ML frameworks. Translate ambiguous problems into structured analytic approaches with measurable impact. 2. ML Engineering & MLOps Execution Build production-grade ML pipelines integrating with WBD’s data ecosystem (Snowflake, AWS, GCP, Databricks). Implement CI/CD, model monitoring, drift detection, alerting, and model performance governance.