Job Opportunities API

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