Remote Data Scientist

About micro1

We are micro1, a leading AI data lab focused on training frontier models and evaluating AI agents. We bring together experts across finance, healthcare, STEM, engineering, and other domains, turning real-world expertise into high-quality training data, evaluations, and feedback loops that help AI systems learn, reason, and perform better. Our platform uses an AI recruiter to identify and vet top talent, allowing expert contributions at scale. This is a remote contractor role, and prior AI experience is not required; we value domain knowledge and the ability to contribute meaningful work to our global expert network.

Requirements

Must have:

• Strong grounding in statistics, mathematics, and data analysis methods.
• Demonstrated experience collecting, organizing, and cleaning large, complex data sets.
• Proficiency in programming languages such as Python, R, or similar tools for data manipulation and modeling.
• Solid data modeling capability, including building and validating predictive models.
• Advanced data visualization skills using tools such as Tableau, Power BI, or comparable libraries.
• Strong attention to detail and a consistent commitment to data quality.
• Excellent written and verbal communication skills, with an emphasis on clarity and precision.

Preferred:

• Experience in remote, cross-functional, or customer-facing teams.
• Experience developing and deploying machine learning solutions in production.

Responsibilities

• Collect, clean, and prepare diverse datasets to ensure they are accurate and ready for analysis.
• Develop, test, and apply statistical models to uncover actionable insights from complex data.
• Perform exploratory data analysis to identify trends, patterns, and opportunities for improvement.
• Present results and recommendations through clear visualizations and reports for both technical and non-technical audiences.
• Work closely with teammates to design and deliver end-to-end data projects from concept to completion.
• Continuously refine analytical approaches and automation processes to improve data workflows.
• Communicate findings effectively in written and spoken formats to ensure recommendations have impact.