About this role
Job Description
We are seeking a Data Scientist with hands-on experience building, deploying, and maintaining production machine learning solutions in a cloud environment. This role will develop scalable ML models, improve the data pipelines that support them, and collaborate with engineering and business stakeholders to deliver data-driven solutions.
This is a contract role supporting a remote, cross-functional team. The successful candidate must have experience taking machine learning models beyond notebook-based development and supporting them in live production environments.
Enterprise experience strongly preferred.
Key Responsibilities
Develop, deploy, and maintain machine learning models in production environments.
Perform exploratory data analysis to identify patterns, opportunities, and modeling approaches.
Conduct feature engineering and prepare data for machine learning workflows.
Build and improve data pipelines supporting model development, deployment, and maintenance.
Monitor production models and help address performance or operational issues.
Collaborate with engineering and business stakeholders to translate business needs into scalable machine learning solutions.
Use version-control and collaborative development practices to manage production code.
Work independently while communicating progress, risks, and technical findings clearly.
Contribute to LLM- or AI-agent-based capabilities where applicable.
Qualifications
Must-Have Skills
At least 2 years of experience building and maintaining production machine learning models.
Strong Python programming skills.
Advanced SQL skills.
Experience deploying machine learning models into production.
Experience monitoring or maintaining models after production deployment.
Experience with AWS SageMaker or another enterprise machine learning platform, such as Vertex AI or Azure Machine Learning.
Experience supporting production machine learning pipelines.
Experience with Git or another version-control system.
Experience performing exploratory data analysis and feature engineering.
Experience building or improving data pipelines that support machine learning workflows.
Ability to work independently in production environments.
Strong communication and cross-functional collaboration skills.
Nice-to-Have Skills
Experience with MLflow, Airflow, dbt, or similar MLOps and workflow tools.
Experience with Snowflake.
Experience supporting marketing or growth use cases.
Experience with experimentation or causal inference.
Experience with large language models.
Experience with AI agents or agentic capabilities.
Experience working in Agile development environments.
Experience developing scalable machine learning solutions in enterprise environments.
Additional Information
Required Tools & Platforms
Python
Advanced SQL
Git or comparable version control
AWS SageMaker, Vertex AI, Azure Machine Learning, or another enterprise ML platform
Production machine learning deployment and monitoring tools
Location, Time & Engagement
Location: Remote, LATAM
Candidates must be located in an approved LATAM country.
The role requires working-hour alignment with a U.S. team operating between Pacific and Eastern time zones.
Schedule: Full-time, approximately 40 hours per week
Engagement type: Contract
Expected contract end date: December 31, 2026
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