Work Type
Remote
Location
Global
Compensation
$245 - $280/hr
About this opportunity
Overview
micro1 is seeking Applied Scientists for a remote contractor project with 100 openings. The work supports a customer data-driven project centered on advanced analytics, experimentation, and helping train next-generation AI systems. The role is part of an open Data Scientist project and places strong emphasis on clear written and verbal communication. No prior AI experience is required if you have the relevant domain knowledge.
What You'll Do
- Design and run statistical analyses to uncover insights from complex datasets.
- Build and validate machine learning models for business or research objectives.
- Use Python and Pandas to develop data models and manage large-scale data transformations.
- Work with stakeholders to define experimentation frameworks and analyze results.
- Document methods, findings, and recommendations clearly and precisely.
- Review peer contributions and provide constructive feedback.
- Deliver reproducible code and well-organized analytical reports.
- Evaluate AI model outputs on statistics, machine learning, experimentation, and quantitative problems.
- Create expert-level prompts, datasets, and reference solutions.
- Identify flawed methodology, statistical errors, and weak quantitative reasoning.
- Provide structured feedback that helps improve model capability.
Requirements
- Strong proficiency in Python, including Pandas; scikit-learn is a plus.
- Experience with applied statistics, experimental design, and machine learning.
- Ability to develop, tune, and evaluate machine learning models in practical settings.
- Solid data modeling skills and experience working with structured and unstructured data.
- Excellent written and verbal communication skills.
- Comfort collaborating on open or cross-functional data science projects.
- Advanced academic background or hands-on work in data science, applied mathematics, or a related field.
- At least one year at a top company in tech, finance, or research is preferred.