General Summary

Sony 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.

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

The successful candidate will likely be someone who:

• 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

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