Data Scientist II - Data Analytics, Hyderabad

Data Scientist II - Data Analytics, Hyderabad

About Warner Bros. Discovery :

Warner Bros. Discovery's Global Technology organization sits at the intersection of technology, entertainment, and everyday utility. Our platform powers experiences across streaming, content delivery, personalization, subscriptions, commerce, and analytics for millions of customers worldwide. As a technology organization, we build scalable, data-driven products that enable Warner Bros. Discovery's continued growth and support our streaming platforms.
For more information, please visit www.wbd.com

Meet Our Team :

The Commerce & Growth Analytics team works across acquisition and retention sciences, partnering with Product, Marketing, Engineering, Finance, and other cross-functional stakeholders to understand how customers discover, subscribe, activate, and retain Warner Bros. Discovery's streaming products. We develop and support strategic analytical products and measurement frameworks, combining advanced analytics, statistics, experimentation, machine learning, and scalable data engineering to generate actionable insights that influence activation, monetization, and revenue growth across global markets. Working with large-scale behavioral, subscription, authentication, and payment data, we solve complex business problems and drive data-informed decision-making across the organization.

About the Role :

As a Data Scientist focused on Retention within Commerce & Growth Analytics, you will be responsible for building machine learning solutions that help understand, predict, and improve customer retention and long-term engagement across WBD's streaming products. You will work on projects from research and exploratory analysis to model development and deployment, following guidance and direction from senior data scientists. You will work closely with cross-functional partners across Product, Design, Commerce, Content, and Marketing to translate retention challenges into data-driven solutions.

Roles & Responsibilities:

• Research, design, and build high-impact end-to-end machine learning systems that drive customer retention and long-term engagement.

• Develop churn, propensity, and customer-lifetime-value (LTV) models to identify at-risk customers and inform retention and win-back strategies.

• Perform exploratory data analysis to uncover behavioral drivers of churn and retention and turn findings into actionable insights.

• Participate in the design of scalable ML solutions in production.

• Promote and role-model best practices of data science, engineering, and communication throughout the organization.

• Evangelize ML adoption and innovation across the product and engineering teams.

• Foster a diverse community of data scientists and ML engineers.

• Partner with cross-functional teammates in Product, Design, Commerce, Content, and Marketing to understand their problems and design solutions.

What to Bring:

• M.S. or B.S. in Computer Science, Statistics, Data Science, Quant, or a related field.

• 3+ years of building products with Machine Learning and Personalization.

• Strong proficiency in SQL and Python (pandas), with hands-on experience using PySpark on large, distributed datasets.

• Strong data wrangling, manipulation, visualization, and EDA skills, with the ability to turn data into actionable insights.

• Strong understanding of the fundamentals of statistical tests and proficiency in classical ML algorithms - linear and logistic regression, bagging, boosting, decision trees, and GBM models.

• Ability to perform model tuning and optimization, and to convert ML outputs into business decisions.

• Production experience implementing machine learning pipelines and models at scale in Python, Java, Scala, or similar languages.

• Demonstrated ability to lead technical decisions.

• Excellent written and verbal communication skills, with the ability to effectively advocate technical solutions to data scientists, engineers, and product manager audiences.

• Passion for data-driven research, development, and experimentation.

• Self-motivated, growth-oriented, and driven to pursue solutions to challenging problems.
  

Good to Have :

• Experience with churn prediction, survival analysis, customer-lifetime-value (LTV) modeling, or engagement and retention analytics.

• Knowledge and experience in one or more of the following: causal inference with machine learning, predictive modeling of long-term customer engagement, ML-driven systems to optimize the customer experience.

• Experience designing and evaluating A/B tests and experimentation frameworks to measure retention and engagement impact.

• Experience with ML frameworks such as TensorFlow, PyTorch, XGBoost, or Scikit-learn.

• Experience with Databricks, Apache Spark, or other distributed data processing frameworks.

• Experience working with subscription-based digital products, streaming services, or consumer analytics.