Assistant Manager-Data Science-Data Scientist

DescriptionJob Duties 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. Interpret complex analytical insights and translate them into clear, actionable business strategies that improve business outcomes.  Support scalable deployment of data products by following best practices in CI/CD processes and contribute to agile project management through tools like Jira for sprint planning, tracking, and team coordination. Collaborate cross-functionally with technical and non-technical stakeholders to gather requirements, define project scope, and lead data science initiatives, demonstrating strong communication, leadership, and team-building skills ResponsibilitiesJob Duties 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. Interpret complex analytical insights and translate them into clear, actionable business strategies that improve business outcomes.  Support scalable deployment of data products by following best practices in CI/CD processes and contribute to agile project management through tools like Jira for sprint planning, tracking, and team coordination. Collaborate cross-functionally with technical and non-technical stakeholders to gather requirements, define project scope, and lead data science initiatives, demonstrating strong communication, leadership, and team-building skills QualificationsMaster'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. 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.