Responsibilities
Applied Machine Learning & Predictive Analytics Develop, 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 Solutions Design 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 & Automation Build 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 & Analytics Work with AWS services such as SageMaker, Redshift, S3, EC2, Lambda, and related technologies to develop, deploy, and operationalize data science solutions
MLOps & Model Lifecycle Management Support 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 Practices Establish strong technical practices across code quality, version control, documentation, testing, model governance, reproducibility, and collaboration with data, analytics, and technology teams
Technical Enablement & Standards Support technical enablement across the team by promoting reusable patterns, shared components, documentation, code quality, production-readiness, and best practices for scalable data science, machine learning, and AI solutions
Technical Outputs, Monitoring & Visualization Create clear, effective, and scalable ways to present model outputs, analytical findings, monitoring metrics, and operational results using Python visualization frameworks, dashboards, reports, or custom analytical tools
Media & Entertainment Applications Apply data science and machine learning to business challenges in media and entertainment, including streaming platforms, theatrical distribution, content performance, TV networks, production, digital media, and audience behavior
Qualifications
Education
Bachelor's degree or advanced degree in Computer Science, Engineering, Statistics, Mathematics, Data Science,



Physics or a related quantitative field.
Languages
Fluent in Spanish & English; Portuguese is a plus.
Experience
Minimum of 8 years of professional experience in data science, machine learning, analytics engineering, machine learning engineering, data engineering, or related technical roles.
Machine Learning & Applied Analytics
Demonstrated experience developing machine learning models for real business applications, including model evaluation, feature engineering, validation, deployment, monitoring, or performance improvement.
Programming & Data Skills
Strong proficiency in Python and SQL is mandatory. Experience working in Linux or command-line environments is a strong plus.
Cloud & Production Experience
Hands‐on experience with cloud-based data and machine learning environments, preferably AWS, including services such as SageMaker, Redshift, S3, EC2, Lambda, or similar tools. Experience with Infrastructure as Code practices, preferably Terraform, is expected. Familiarity with Azure, GCP, or multicloud data and machine learning environments is a plus.
MLOps & Production Practices
Familiarity with technical production practices applied to machine learning, including Git‐based workflows, testing, CI/CD concepts, containerization, dependency management, model monitoring, and production support. Experience with feature stores, data drift monitoring, model performance tracking, or model retraining workflows is a strong plus.
ML Frameworks & Tooling
Experience with machine learning libraries and frameworks such as Scikit‐learn, TensorFlow, PyTorch, XGBoost, LightGBM, or similar.
Generative AI, NLP & Computer Vision Experience
Experience with Generative AI, large language models, NLP, computer vision, embeddings, vector search, prompt engineering, RAG architectures, image/video analysis, multimodal AI, or AI-assisted workflow automation is a plus.
Data Engineering Foundations
Familiarity with data pipelines, APIs, batch processing, orchestration, data quality checks, version control, and scalable analytical workflows. Experience with workflow orchestration tools such as Airflow, AWS Step Functions, Prefect, Dagster,



or similar is a plus.
Statistical & Analytical Foundation
Strong understanding of statistical analysis, including regression, hypothesis testing, time series, forecasting, experimentation, and model interpretation.
Visualization & Technical Communication
Ability to communicate analytical results, model behavior, technical decisions, and operational outputs clearly through documentation, visualizations, dashboards, and structured technical explanations.
Technical Ownership & Collaboration
Strong technical ownership, with the ability to design solutions, coordinate implementation efforts, review technical work, promote reusable patterns, and collaborate effectively with data, analytics, and technology teams.
Language Skills
Excellent written and verbal communication skills in English are mandatory. Spanish proficiency is a plus.
Industry Experience
Experience in Media and/or Entertainment is a plus, especially in streaming platforms, production studios, theatrical distribution, TV channels, digital media, social media, marketing analytics, or audience insights.
Preferred Profile
Enjoys writing clean, maintainable Python code, not just notebooks
Has experience taking models or analytical solutions beyond experimentation
Understands that useful data science solutions depend on reliability, adoption, repeatability, and maintainability
Can work with messy real‐world data and build practical, scalable solutions
Is comfortable working with pipelines, cloud services, automation, monitoring, and production-oriented workflows
Is curious about emerging AI capabilities, including GenAI, NLP, computer vision, and multimodal applications
Can explain technical trade‐offs clearly without needing to be the primary business-facing interface
Brings a builder mindset: pragmatic, curious, structured, and accountable
Hi, we're Sony Pictures Entertainment We are in the business of creativity ... making some of the most beloved film and television of all time for every platform in the world. As the most creative and proudly independent studio, our future is boundless. Sony Pictures Entertainment is a division of Sony Corporation, a creative entertainment company built on a foundation of technology. Along with our sister companies, we make movies, television, music and games that engage billions of people, connecting creators and audiences around the globe. We are looking for innovators to join us as we forge the future of entertainment!
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📌 Data Science Manager (Buenos Aires)
🏢 Sony Pictures Entertainment
📍 Buenos Aires