Minimize subscriber churn and accelerate subscription growth for media and cable networks at NBCUniversal
Requirements
- Advanced skills in Python/R, SQL, and experience with ML libraries (scikit-learn, XGBoost, TensorFlow/PyTorch)
- Strong background in building predictive churn models, CLV, causal inference and uplift modeling
- Leverage cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Spark, Hive, Databricks), staying current with evolving technologies and Databricks’ architecture for scalable data science workflows
- Familiarity with third party data such as comScore, Nielsen, IMDb, Gracenote, or similar platforms to enhance content performance analysis and audience insights
- Experience in content analytics within subscription-based media and direct-to-consumer (DTC) products
- Experience building personalization engines or recommendation systems in digital subscription environments
Responsibilities
- Leverage structured and unstructured data from various media and entertainment sources to prepare datasets for advanced analytics and modeling
- Develop and deliver impactful analytical tools and solutions leveraging statistical modeling, machine learning, and data science to uncover business insights and support strategic decision-making
- Design and apply advanced predictive and machine learning models; including clustering (K-means, hierarchical), classification (KNN, Naive Bayes, CART), time series forecasting, logistic regression, and econometric models to optimize pricing strategies, assess price elasticity, segment customers, and enhance revenue across channels
- Leverage generative AI and large language models (LLMs) to develop and implement personalized content and messaging strategies across diverse media channels, enhancing audience engagement and campaign effectiveness
- Assess and validate statistical models using appropriate performance metrics to ensure precision and accuracy such as accuracy, sensitivity, specificity, ROC, AUC
- Analyze consumer behavior trends and shifts across various digital touchpoints; perform cross-channel attribution analysis to inform targeted retention strategies
- Monitor and analyze key engagement metrics to assess the performance of subscriber onboarding programs and their impact on long-term retention
Other
- Master’s or PhD in data Science, statistics, computer science, or related quantitative field
- 5+ years’ experience in data science roles with demonstrated impact on retention, engagement, or churn reduction
- Travel may be required for this position for company or department meetings
- Hybrid: This position has been designated as hybrid, generally contributing from the office a minimum of three days per week
- Strong communication, leadership, and team-building skills
