Data Science Manager

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
Bachelors 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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