About this role
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.
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.
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
Tech stack
LLMPythonAWSAzureAirflowdbt