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.

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.
• Contribute to the design of feature stores, experimentation platforms, and ML observability frameworks.
• Work closely with data engineering and platform teams to ensure scalable, reliable model deployment.

3. Applied Data Science Delivery for High-Value Business Problems Lead delivery of ML solutions for domains such as:

• Content performance and ratings prediction
• Audience segmentation, churn and lifetime value modeling
• Search ranking, discovery, and personalization systems
• Metadata enrichment and content understanding using NLP & CV
• Operational forecasting and automation Convert analytical outcomes into actionable insights-delivering high-ROI recommendations to product and business stakeholders.

4. Stakeholder Partnership & Communication

• Partner with senior leaders across Streaming, Technology, Product, Content, and Marketing.
• Present complex modeling results clearly to non-technical audiences, influencing business decisions with data-driven insights.
• Shape and guide analytical roadmaps aligned to enterprise priorities.

5. Leadership, Collaboration & Mentoring

• Mentor data scientists and ML engineers, elevating scientific rigor and best practices across the team.
• Lead project teams in experimentation, modeling, evaluation, and productionization.
• Promote knowledge sharing, documentation standards, and internal communities of practice.
• Champion responsible AI, model governance, and ethical data usage across engagements.

Qualifications & Experiences:

• Master’s degree (or Ph.D. preferred) in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related fields.
• 14-16 years of total experience, with 10-12 years in Data Science, ML, and advanced analytics.
• Strong hands-on expertise in:
  • Predictive modeling, optimization, deep learning, NLP, and CV
  • Python, SQL, PyTorch/TensorFlow, Scikit-learn, ML frameworks
  • ML Ops, model deployment, monitoring, and observability
  • Statistical modeling, experiment design, Bayesian methods
  • Cloud platforms (AWS/GCP/Snowflake)
• Demonstrated track record of delivering business impact through ML solutions in large-scale environments.
• Strong communication skills with demonstrable ability to influence cross-functional teams.

Preferred:

• Experience in Media & Entertainment, streaming, digital advertising, metadata, or audience intelligence.
• Hands-on exposure to foundation models, LLMs, embeddings, or generative AI.
• Experience with video intelligence, OCR/CV pipelines, or content metadata engines.
• Publications, patents, or conference-level contributions in ML/AI.

How We Get Things Done…

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.

Championing Inclusion at WBD

If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.