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 InformationRequired 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
Skills Required
- 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 ML platform (Vertex AI, 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.
- 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.
- Located in an approved LATAM country.
- Ability to align working hours with a U.S. team (Pacific to Eastern time zones).
What We Do
Lifted, an Upwork Company, is a B2B SaaS platform that helps enterprise organizations source, contract, manage, and pay contingent talent globally and compliantly. It supports multiple engagement models, including independent contractors, staff augmentation, and employer-of-record, while integrating with MSPs and VMSs to provide centralized visibility, spend control, and audit-ready compliance, delivering a white-labeled talent experience for hiring managers and contingent workforce programs.
