Principal Data Scientist

Warner Bros Discovery Warner Bros Discovery · Media · Hyderabad, Telangāna, India · Technology

Principal Data Scientist role focused on architecting, developing, and deploying high-impact machine learning and data science solutions for Warner Bros. Discovery's global businesses. The role involves translating complex business problems into scalable analytical solutions using predictive modeling, optimization, experimentation, NLP, and computer vision. Key responsibilities include leading end-to-end ML model development, implementing MLOps practices for production-grade pipelines, and delivering ML solutions for domains like content performance, audience behavior, search, and metadata enrichment. Experience in Media & Entertainment is a plus, and hands-on exposure to foundation models, LLMs, or generative AI is preferred.

What you'd actually do

  1. Design, build, and scale advanced machine learning models across predictive analytics, NLP, CV, forecasting, optimization, and recommender systems.
  2. Build production-grade ML pipelines integrating with WBD’s data ecosystem (Snowflake, AWS, GCP, Databricks).
  3. 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
  4. Partner with senior leaders across Streaming, Technology, Product, Content, and Marketing.
  5. Mentor data scientists and ML engineers, elevating scientific rigor and best practices across the team.

Skills

Required

  • Machine learning
  • Data science
  • Statistical modeling
  • Predictive analytics
  • NLP
  • Computer Vision
  • Forecasting
  • Optimization
  • Recommender systems
  • Feature engineering
  • Model selection
  • Hyperparameter tuning
  • Validation
  • ML frameworks
  • MLOps
  • Model deployment
  • Model monitoring
  • Drift detection
  • Alerting
  • Model performance governance
  • Feature stores
  • Experimentation platforms
  • ML observability
  • Data pipelines
  • Python
  • SQL
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • AWS
  • GCP
  • Snowflake
  • Experiment design
  • Causal inference
  • Bayesian methods
  • Communication skills
  • Leadership

Nice to have

  • Media & Entertainment experience
  • Streaming experience
  • Digital advertising experience
  • Metadata experience
  • Audience intelligence experience
  • Foundation models
  • LLMs
  • Embeddings
  • Generative AI
  • Video intelligence
  • OCR/CV pipelines
  • Content metadata engines
  • Publications
  • Patents
  • Conference-level contributions in ML/AI

What the JD emphasized

  • 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
  • ML Ops, model deployment, monitoring, and observability
  • Cloud platforms (AWS/GCP/Snowflake)
  • Demonstrated track record of delivering business impact through ML solutions in large-scale environments.

Other signals

  • end-to-end development of ML models
  • production-grade ML pipelines
  • MLOps practices
  • scalable, reliable model deployment
  • delivering business impact through ML solutions