Data Scientist II, Hyderabad

As a Data Scientist, the role is a hands-on modelling and experimentation leadership position for WBD's consumer platforms, translating complex product and business questions into measurable data science opportunities. The postholder builds models and evaluation frameworks and partners with Machine Learning Engineering to bring validated solutions into production. The work directly influences identity resolution, audience intelligence, personalization, retention, engagement, advertising effectiveness, and customer experience across WBD's global streaming platforms.

The role sits within the SPARK organisation, where Hyderabad-based data science and machine learning teams transform first-party audience signals into capabilities for identity, audience intelligence, advertising, personalization, forecasting, engagement, and retention.

• 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.
• Design experiments, evaluation frameworks, baselines, success metrics, and promotion criteria that measure both model quality and business impact.
• 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.
• Work with Product, Engineering, Data Science, Data Engineering, Ad Sales, and business stakeholders to turn ambiguous opportunities into well-scoped data science solutions.
• Communicate findings, tradeoffs, risks, and recommendations clearly to technical and non-technical audiences.
• Help define reusable standards for feature and label quality, leakage prevention, model evaluation, documentation, experimentation rigor, and production readiness.
• Frame the right problems with Machine Learning Engineering, Product, Data Engineering, Ad Sales, and business teams, and turn insight into measurable product and business impact.
• Use Databricks, Snowflake, and AWS as primary working platforms, alongside modern AI and agentic development workflows where they improve quality, speed, and repeatability.

• 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.
• 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.

• Experience with Spark or large-scale distributed data processing is strongly preferred.
• 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,…