Overview

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

Responsibilities

• Design and operate low latency online serving systems for fraud scoring, message decisioning and real-time personalization
• Build ML models for identity resolution, audience intelligence, content affinity modeling, genre-preference modeling and time-series forecasting across global markets
• Integrate with personalization systems to consume in-app user signals
• Design feature pipelines that fuse real-time streaming signals with batch-computed features for online scoring
• Partner with Product, Engineering, and Data Science to translate business problems into well-scoped ML solutions
• Evaluate new technologies and approaches, including DCR-native modeling, graph ML, agentic ML orchestration, and LLM-augmented pipelines, with clear build/buy/partner recommendations.
• Architect probabilistic identity resolution systems that connect unauthenticated device IDs and first-party cookies to households/persons with calibrated confidence across WBD brands.
• Lead the evolution of Audience Intelligence, including ML Promo Optimizer, STAT v2, lookalike modeling inside Snowflake DCR, and content segmentation.
• Own ML architecture for forecasting use cases such as audience growth, demand, yield, and pricing, ensuring models are monitored and continuously improved.
• Bring ML personalization signals, such as genre/content affinity and engagement trends, into batch and future real-time activation paths.
• Design offline and online evaluation approaches with clear baselines, success metrics, experiment design, and promotion criteria.
• Improve feature and label quality, leakage prevention, bias checks, calibration, explainability where applicable, and impact measurement.
• Identify technical risks early, document tradeoffs, and drive mitigation plans with engineering, data, product, and business partners.
• Turn incidents and postmortems into reusable standards, automated checks, and platform improvements.
• Mentor MLE 2 engineers through architecture reviews, design discussions, implementation guidance, and hands-on problem solving.
• Partner with the ML leads on technical roadmap shaping, execution planning, capability development, and technical input into hiring profiles.
• Represent the Hyderabad team in technical forums with ML, Data Engineering, Product, Ad Sales, and US-based stakeholders.
• Create clear design docs, architecture reviews, readiness reviews, and postmortems that strengthen engineering culture.

Qualifications

• 5 - 8 years of ML engineering experience, or 2+ years with a Ph.D., with demonstrated Senior-level scope, technical ownership, and cross-team impact.
• You are comfortable owning systems end-to-end, from problem definition through serving and monitoring, not just the modeling layer.
• You have worked on optimization problems like ranking, personalization, or multi-objective decisioning in environments with interacting metrics.
• You have hands on experience with Databricks, Spark and Sagemaker.
• Expert proficiency with ML frameworks (PyTorch, TensorFlow, XGBoost/LightGBM, scikit-learn) and deep understanding of statistics and ML fundamentals.
• Master’s or Ph.D. in Computer Science, Statistics, Machine Learning, or a related field (or equivalent industry experience).
• Excellent communication skills, with the ability to explain technical solutions to engineering, science, product, and executive audiences.
• Streaming / Identity / Fraud / Ad-tech ML: identity resolution, audience modeling, recommendation/ranking, content understanding.
• Hands-on experience with agentic AI frameworks at production scale
• Published research or conference presentations in relevant ML domains.
• Primary platform: AWS , Databricks
• Warehouse: Snowflake
• Activation: Mosaic, FreeWheel, Google Ad Manager.
• Languages: Python (primary), SQL, Scala (as needed).

Benefits

• A Great Place to work
• Equal opportunity employer
• Fast track growth opportunities