Data Scientist II, Hyderabad

Data Scientist II, Hyderabad . Our Data Scientists partner closely with Machine Learning Engineering, Product, Data Engineering, Ad Sales, and business teams to frame the right problems, build rigorous models and measurement approaches, and turn insight into measurable product and business impact. We work primarily with Databricks, Snowflake, and AWS, and we are early adopters of modern AI and agentic development workflows where they help us improve quality, speed, and repeatability. As a Data Scientist, you will be a hands-on modeling and experimentation leader for WBD’s consumer platforms. You will translate complex product and business questions into measurable data science opportunities, build models and evaluation frameworks, and partner with Machine Learning Engineering to bring validated solutions into production. Your work will directly influence identity resolution, audience intelligence, personalization, retention, engagement, advertising effectiveness, and customer experience across WBD's global streaming platforms. Data Science & Modeling: Build and improve models for probabilistic identity resolution, audience modeling, segmentation, content affinity, genre-preference modeling, forecasting, fraud and abuse detection, promo optimization, personalization, and lifecycle engagement. Production Partnership: Partner with Machine Learning Engineers to move validated models into scalable production pipelines with clear expectations for reproducibility, monitoring, drift detection, data quality, calibration, and long-term model health. Insight to Impact: Communicate findings, tradeoffs, risks, and recommendations clearly to technical and non-technical audiences. Technical Quality: Help define reusable standards for feature and label quality, leakage prevention, model evaluation, documentation, experimentation rigor, and production readiness. 3–5 years of hands-on experience building data science, statistical modeling, or machine learning solutions for real-world products or business systems. Strong ability to translate business problems into hypotheses, data science problem statements, success metrics, and actionable recommendations. Deep practical knowledge of statistical modeling, machine learning fundamentals, causal inference, experimentation, forecasting, ranking, recommendation, calibration, and model evaluation. Strong programming and analytical skills in Python and SQL; experience with Spark or large-scale distributed data processing is strongly preferred. Hands-on experience with Databricks, Snowflake, AWS, MLflow, notebooks, experiment tracking, or similar data and ML tools. Experience partnering with Machine Learning Engineering or platform teams to operationalize models, . Ability to collaborate effectively with Product, Engineering, Data Engineering, Machine Learning Engineering, Finance, Ad Sales, and business stakeholders in a global environment. Excellent communication skills, including the ability to explain technical methods, model tradeoffs, uncertainty, and business implications to varied audiences. Enjoy turning ambiguous business problems into clear, measurable opportunities for data science and machine learning. Care deeply about experimentation quality, model reliability, and real-world impact. Influence technical direction through collaboration, evidence, and sound judgment. Enjoy working across data science, engineering, product, and business teams to solve meaningful problems. Primary platforms: Databricks, AWS . Languages: Python, SQL; Scala as needed . ML and data tools: Spark, MLflow, notebooks, experiment tracking, model evaluation frameworks . Activation contexts: Personalization systems, Mosaic, FreeWheel, Google Ad Manager, and related audience activation platforms where applicable . Master’s degree or Ph.D. in Computer Science, Statistics, Machine Learning, Data Science, Engineering, Mathematics, Economics, or a related quantitative field. Experience in streaming, media, advertising technology, consumer identity, fraud, recommendations, personalization, forecasting, lifecycle engagement, or audience intelligence. Experience with uplift modeling, causal impact analysis, lookalike modeling, clean-room or DCR analysis, graph-based methods, or multi-objective optimization. Experience building reusable evaluation frameworks, model quality standards, or scalable data science patterns across teams. Championing Inclusion at WBD. What You’ll Do. Communicate clearly with both technical and non-technical partners.