Principal Data Scientist

The Principal Data Scientist will architect, develop, and deploy high-impact machine learning and data science solutions across Warner Bros. Discovery's global businesses. The role translates complex business problems into scalable analytical solutions using predictive modelling, optimisation, experimentation, NLP, computer vision, and modern ML engineering practices.

• Design, build, and scale advanced machine learning models across predictive analytics, NLP, computer vision, forecasting, optimisation, and recommender systems.
• Apply statistical foundations including hypothesis testing, probability modelling, causal inference, and experiment design to solve complex business problems.
• Develop interpretable and explainable ML models, ensuring scientific rigour, 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.
• Build production-grade ML pipelines integrating with WBD's data ecosystem (Snowflake, AWS, GCP, Databricks), and 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, working closely with data engineering and platform teams to ensure scalable, reliable model deployment.
• Lead delivery of ML solutions for domains such as content performance and ratings prediction, audience segmentation, churn and lifetime value modelling, search ranking, discovery, and personalisation, metadata enrichment and content understanding using NLP and CV
• And operational forecasting and automation.
• Convert analytical outcomes into actionable insights and present complex modelling results clearly to non-technical audiences, influencing business decisions with data-driven insights.
• Partner with senior leaders across Streaming, Technology, Product, Content, and Marketing, and shape analytical roadmaps aligned to enterprise priorities.
• Mentor data scientists and ML engineers, lead project teams in experimentation, modelling, evaluation, and productionisation, and promote knowledge sharing, documentation standards, and internal communities of practice.
• Champion responsible AI, model governance, and ethical data usage across engagements.

• Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field.
• 14–16 years of total experience, including 10–12 years of deep, hands-on experience in applied data science, machine learning, and statistical modelling.
• Strong hands-on expertise in predictive modelling, optimisation, deep learning, NLP, and computer vision.
• Strong hands-on expertise in Python, SQL, PyTorch/TensorFlow, Scikit-learn, and ML frameworks.
• Strong hands-on expertise in ML Ops, model deployment, monitoring, and observability.
• Strong hands-on expertise in statistical modelling, experiment design, and Bayesian methods.
• Strong hands-on expertise in 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.
• Strong technical expertise, business acumen, and problem-solving skills.
• Ability to guide cross-functional partners across Product, Engineering, Technology, and Business domains.

• Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field.
• 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.