Sr. Principal Data Scientist

Warner Bros. Discovery•Atlanta, GA

About The Position

As a Sr. Principal Data Scientist, you will operate at the highest technical individual-contributor level at Warner Bros. Discovery—serving as a company-wide authority in advanced Data Science, Machine Learning, and Applied AI. This role is designed for an elite practitioner with 15–18+ years of experience, including 10–14 years of deep, hands-on expertise in Data Science, ML, and AI systems at enterprise scale. Unlike leadership or management roles, this position is purely an IC role, focused on technical depth, architectural rigor, and scientific excellence, without formal people-management responsibilities. You will design, architect, and deliver some of WBD’s most complex and business-critical AI systems, directly influencing how the company creates, distributes, personalizes, monetizes, and optimizes content across streaming, linear TV, advertising, and direct-to-consumer platforms. This is a hands-on, high-impact role for a technologist who thrives on solving unsolved problems, pushing the boundaries of applied ML, and translating advanced science into durable business advantage.

Requirements

Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Operations Research, or related disciplines.
18–20 years of total experience, with 13–15 years in Data Science/ML, including hands-on technical leadership.
Deep expertise in: Predictive modeling, optimization, and advanced ML techniques, MLOps and large-scale model deployment, Modern cloud ecosystems (AWS/GCP/Snowflake), Python, PyTorch, TensorFlow, SQL, ML frameworks, Experiment design, causal inference, and statistical modeling.
Demonstrated experience in Media & Entertainment, streaming, digital advertising, or consumer intelligence.
Strong track record of delivering enterprise-impact through AI solutions.
Exceptional communication skills, including the ability to influence executives and inspire technical teams.

Nice To Haves

Experience developing or customizing Large Language Models or multimodal foundation models.
Patent, publication, or conference-track record in ML/AI.
Experience with video intelligence, CV for media workflows, or content metadata systems.
Experience partnering with product and engineering organizations in a fast-paced environment.

Responsibilities

Act as one of WBD’s most senior technical ICs in Data Science and Machine Learning. Lead the end-to-end design and implementation of advanced ML systems across: Content intelligence & metadata enrichment, Audience modelling & personalization, Forecasting, optimization, and experimentation, Advertising intelligence & monetization analytics. Set technical direction and standards for complex ML implementations without direct people management.
Design and implement state-of-the-art models, including: Large-scale recommender systems, Time-series forecasting & probabilistic models, Causal inference, experimentation & uplift modelling, NLP, generative AI & multimodal ML systems, Computer vision & video intelligence pipelines. Apply rigorous statistical thinking, experimentation discipline, and scientific validation to all solutions. Serve as a final technical reviewer for high-risk or high-impact ML solutions.
Architect production-grade ML systems integrated with WBD’s cloud data ecosystem (AWS, Snowflake, GCP). Define best practices for: Feature engineering & feature stores, Model lifecycle management & MLOps, CI/CD for ML, model monitoring, and drift detection, Reproducibility, governance, and responsible AI. Partner deeply with data engineering, platform, and product engineering teams to ensure scalable, resilient delivery.
Own and deliver mission-critical AI solutions across: Content performance prediction & ratings intelligence, Marketing attribution & lifecycle analytics, Search, discovery & ranking systems, Ad load optimization & pricing intelligence, Operational forecasting & automation. Translate complex modelling outputs into clear, executive-ready insights that drive decisions.
Serve as a trusted technical advisor to senior leaders across Streaming, Content, Ad Sales, Marketing, and Technology. Communicate complex ML concepts with clarity and credibility to non-technical stakeholders. Influence enterprise AI roadmaps, architectural decisions, and investment priorities through expertise—not hierarchy.
Mentor senior and staff-level data scientists through technical guidance, design reviews, and deep problem-solving. Contribute to internal AI communities of practice, technical forums, and standards bodies. Elevate overall engineering and scientific rigor across the Data Science organization.