Data Scientist II

Job Summary

Data Scientist II, Hyderabad

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.

Responsibilities

• 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.
• Experimentation & Measurement: Design experiments, evaluation frameworks, baselines, success metrics, and promotion criteria that measure both model quality and business impact.
• 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.
• Business Problem Framing: Work with Product, Engineering, Data Science, Data Engineering, Ad Sales, and business stakeholders to turn ambiguous opportunities into well-scoped data science solutions.
• 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.

Requirements

• 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.

You'll Be Successful If You

• 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.
• Communicate clearly with both technical and non-technical partners.
• Influence technical direction through collaboration, evidence, and sound judgment.
• Enjoy working across data science, engineering, product, and business teams to solve meaningful problems.

Our Technology Stack

• Primary platforms: Databricks, AWS

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