Principal Data Scientist

Role overview

Principal Data Scientist at Warner Bros. Discovery

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.

Responsibilities

• You will lead end-to-end development of ML models, champion scientific rigor, and ensure robust operationalization through modern MLOps practices.
• Conduct feature engineering, model selection, hyperparameter tuning, and validation using modern ML frameworks.
• 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.
• Lead project teams in experimentation, modeling, evaluation, and productionization.

Preferred qualifications and skills

• 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.

Employment details

• Location: Hyderabad [Telangāna] [India]

Application details

• Apply strong statistical foundations—hypothesis testing, probability modeling, causal inference, and experiment design—to solve complex business problems.

Additional role details

• 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.
• You will translate complex business problems into scalable analytical solutions—leveraging predictive modeling, optimization, experimentation, NLP, computer vision, and modern ML engineering 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.
• Core Data Science & Machine Learning Expertise.
• Design, build, and scale advanced machine learning models across predictive analytics, NLP, CV, forecasting, optimization, and recommender systems.
• Develop interpretable and explainable ML models, ensuring scientific rigor, reproducibility, and operational robustness.
• Translate ambiguous problems into structured analytic approaches with measurable impact.
• ML Engineering & MLOps Execution.
• Build production-grade ML pipelines integrating with WBD’s data ecosystem (Snowflake, AWS, GCP, Databricks).
• 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.
• 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.
• Leadership, Collaboration & Mentoring.
• Mentor data scientists and ML engineers, elevating scientific rigor and best practices across the team.
• Promote knowledge sharing, documentation standards, and internal communities of practice.
• Champion responsible AI, model governance, and ethical data usage across engagements.
• 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.
• 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.
• Championing Inclusion at WBD.