Applied Scientist

micro1; United States; Remote; USD 83,000 - 124,000; Part time

Job summary

micro1 is seeking an Applied Scientist for a remote contractor role focused on analytics and experimentation. You will train next-generation AI systems and shape model learning, reasoning, and performance through real-world input.

The position emphasizes high-quality, reproducible code and precise written and verbal communication, with collaboration across stakeholders to define experiments and deliver actionable insights.

Qualifications

• Experience in applied statistics and experimental design.
• Proven ability to build, tune and evaluate ML models in practical settings.
• Strong data modeling skills across structured and unstructured data.
• Excellent written and verbal communication with clear technical documentation.

Responsibilities

• Design and execute statistical analyses to uncover actionable insights from complex datasets.
• Develop and validate machine learning models for business or research objectives.
• Apply data modeling techniques and manage large-scale data transformations using Python and Pandas.
• Collaborate with stakeholders to define experimentation frameworks and analyze outcomes.
• Document methodology, findings and recommendations with clarity and precision.
• Review peer contributions and provide high-quality feedback to support knowledge sharing.
• Deliver reproducible code and organized analytical reports as project deliverables.
• Evaluate AI model outputs on statistics, ML, experimentation and quantitative problems.
• Create expert prompts, datasets and reference solutions.
• Identify flawed methodology, statistical errors, and weak quantitative reasoning.
• Provide clear, structured feedback that improves model capability.

Skills

Applied statistics

Experimental design

Machine learning

Data modeling

Communication

Tools

Python

Pandas

scikit-learn

Job description

Role Title: Applied Scientist

Role Type: Contractor

Location: Remote

micro1 is engaging Applied Scientists to contribute to a customer's data-driven project, focusing on advanced analytics and experimentation. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required; your domain knowledge is what matters. This opportunity is part of an open Data Scientist project, with a strong emphasis on clear written and verbal communication.

Scope of Work

1. Design and execute statistical analyses to uncover actionable insights from complex datasets.
2. Develop and validate machine learning models for various business or research objectives.
3. Apply data modeling techniques and manage large-scale data transformations using Python and Pandas.
4. Collaborate with stakeholders to define experimentation frameworks and analyze outcomes.
5. Document methodology, findings, and recommendations with a focus on clarity and precision.
6. Review peer contributions and provide high-quality feedback to support knowledge sharing.
7. Deliver reproducible code and well-organized analytical reports as part of the project deliverables.
8. Evaluate AI model outputs on statistics, machine learning, experimentation, and quantitative problems.
9. Create expert-level prompts, datasets and reference solutions.
10. Identify flawed methodology, statistical errors, and weak quantitative reasoning.
11. Provide clear, structured feedback that improves model capability.

Preferred Qualifications

1. At least one year at a top company (tech, finance, or research).
2. Expertise in Python, including proficiency with libraries such as Pandas and scikit-learn (sklearn).
3. Demonstrated experience in applied statistics, experimental design, and machine learning.
4. Proven ability to develop, tune, and evaluate machine learning models in practical settings.
5. Strong data modeling skills and experience managing structured and unstructured data.
6. Excellent written and verbal communication skills, with a passion for clear technical documentation.
7. Experience collaborating on open or cross-functional data science projects is a plus.
8. Advanced academic background or hands-on work in data science, applied mathematics, or related fields.