3 days ago

Data Science Manager

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Lead LATAM data science and advanced analytics by building production-ready machine learning solutions that combine forecasting, audience and content analytics, and data-driven decision support. Manage end-to-end MLOps workflows—from model development and deployment to monitoring, documentation, and continuous improvement—using AWS services like SageMaker and Redshift. Partner with analytics, data, and technology teams to deliver scalable, reliable pipelines and reusable components across distribution, production, digital, and streaming initiatives.

Location: Buenos Aires, Bogotá

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Manager level

Key Responsibilities

•Develop, evaluate, and improve machine learning models to support forecasting, audience analysis, content performance, sales planning, marketing optimization, and related use cases.
•Design and implement production-ready, scalable, and maintainable data science/ML solutions, including deployment, batch scoring, inference workflows, and automated pipelines.
•Build and maintain data processing pipelines, feature engineering workflows, scoring routines, and APIs/batch services using Python, SQL, version control, and cloud-based tools.
•Lead the ML lifecycle (experimentation, packaging, deployment, monitoring, retraining, versioning, documentation, and production support) with practices for feature management and model performance monitoring.
•Provide technical leadership and enablement across code quality, version control, documentation, testing, model governance, reproducibility, and collaboration with analytics, data, and technology partners.

Key Requirements

•Minimum 8 years of professional experience in data science, machine learning, analytics engineering, machine learning engineering, data engineering, or related technical roles.
•Strong proficiency in Python and SQL, with experience in Linux/command-line environments preferred.
•Hands-on experience with cloud-based data and machine learning environments, preferably AWS (SageMaker, Redshift, S3, EC2, Lambda).
•Experience with MLOps/production practices including Git-based workflows, testing, CI/CD, containerization, dependency management, and model monitoring/production support.
•Bachelor’s degree (or advanced degree) in Computer Science, Engineering, Statistics, Mathematics, Data Science, Physics, or a related quantitative field.

Experience:8+ yearsMedia and entertainmentStreaming

Education:Bachelor's

Skills:Technical ownershipCollaborationCommunicationBuilder mindset

Languages:SpanishEnglishPortuguese

Tech Stack:PythonSQLAWSSageMakerRedshiftS3EC2LambdaTerraformLinuxGitCI/CDContainersModel monitoringData drift detectionFeature storesScikit-learnTensorFlowPyTorchXGBoost

Company Brief

Sony Pictures

Sony Pictures is a major film and television studio producing, financing, acquiring, and distributing motion pictures, television programming, and digital content globally across multiple labels and platforms.

Industry: Film & Television

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Culver City, United States

Founded: 1987

Website LinkedIn

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Sony Pictures

3 days ago

Data Science Manager

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Location: Buenos Aires, Bogotá

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Manager level

Key Responsibilities

•Develop, evaluate, and improve machine learning models to support forecasting, audience analysis, content performance, sales planning, marketing optimization, and related use cases.
•Design and implement production-ready, scalable, and maintainable data science/ML solutions, including deployment, batch scoring, inference workflows, and automated pipelines.
•Build and maintain data processing pipelines, feature engineering workflows, scoring routines, and APIs/batch services using Python, SQL, version control, and cloud-based tools.
•Lead the ML lifecycle (experimentation, packaging, deployment, monitoring, retraining, versioning, documentation, and production support) with practices for feature management and model performance monitoring.
•Provide technical leadership and enablement across code quality, version control, documentation, testing, model governance, reproducibility, and collaboration with analytics, data, and technology partners.

Key Requirements

•Minimum 8 years of professional experience in data science, machine learning, analytics engineering, machine learning engineering, data engineering, or related technical roles.
•Strong proficiency in Python and SQL, with experience in Linux/command-line environments preferred.
•Hands-on experience with cloud-based data and machine learning environments, preferably AWS (SageMaker, Redshift, S3, EC2, Lambda).
•Experience with MLOps/production practices including Git-based workflows, testing, CI/CD, containerization, dependency management, and model monitoring/production support.
•Bachelor’s degree (or advanced degree) in Computer Science, Engineering, Statistics, Mathematics, Data Science, Physics, or a related quantitative field.

Experience:8+ yearsMedia and entertainmentStreaming

Education:Bachelor's

Skills:Technical ownershipCollaborationCommunicationBuilder mindset

Languages:SpanishEnglishPortuguese

Tech Stack:PythonSQLAWSSageMakerRedshiftS3EC2LambdaTerraformLinuxGitCI/CDContainersModel monitoringData drift detectionFeature storesScikit-learnTensorFlowPyTorchXGBoost

Company Brief

Sony Pictures

Industry: Film & Television

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Culver City, United States

Founded: 1987

Website LinkedIn