Data Science Manager
General SummarySony Pictures Entertainment is looking for a hands-on and technically strong Data Science Manager to join our LATAM Data Science & Advanced Analytics team in Buenos Aires Argentina or Bogota Colombia.This role will lead the development of scalable data science solutions that combine machine learning, analytics, and cloud-based deployment to support business decision-making across Distribution, Networks, Production, Digital, and Streaming-related initiatives.The ideal candidate is not only comfortable building predictive models, but also enjoys transforming analytical ideas into reliable, reusable, and production-ready solutions. This person should bring strong Python and SQL skills, experience with cloud environments, solid understanding of machine learning workflows, and the ability to collaborate with analytics, data, and technology partners.The role requires someone who can combine data science judgment with strong technical discipline, building solutions that are not only analytically sound but also reliable, maintainable, and scalable in real-world business environments.This is a hands-on technical leadership role for someone who can move from data exploration and modeling to deployment, monitoring, documentation, automation, and continuous improvement, while helping establish scalable, production-ready patterns for data science, machine learning, and AI solutions.ResponsibilitiesApplied Machine Learning & Predictive AnalyticsDevelop, evaluate, and improve machine learning models to support forecasting, audience analysis, content performance, sales planning, marketing optimization, and other operational and analytical use cases Production-Ready Data Science SolutionsDesign and implement robust, scalable, and maintainable data science and machine learning solutions, including model deployment, batch scoring, inference workflows, automated pipelines, monitoring routines, reusable components, and continuous improvement processes Data Pipelines & AutomationBuild and maintain data processing pipelines, feature engineering workflows, model scoring routines, APIs or batch services, and automated analytical processes using Python, SQL, version control, and cloud-based tools Cloud-Based Machine Learning & AnalyticsWork with AWS services such as SageMaker, Redshift, S3, EC2, Lambda, and related technologies to develop, deploy, and operationalize data science solutions MLOps & Model Lifecycle ManagementSupport the full lifecycle of machine learning solutions, including experimentation, experiment tracking, packaging, deployment, monitoring, retraining, versioning, documentation, and production support. Help implement practices for feature management, model performance monitoring, data drift detection, model degradation analysis, and continuous model improvement Technical Leadership & Best PracticesEstablish strong technical practices across code quality, version control, documentation, testing, model governance...