Fully remote contractor role ($245–$280/hr) evaluating AI-generated outputs on expert data science, statistics, machine learning, and quantitative reasoning problems for an AI lab. Day to day: writing expert prompts and datasets, building reference solutions, spotting statistical flaws, and giving structured feedback to improve model performance.
micro1 is partnering with a leading AI lab to engage experienced Data Scientists and quantitative professionals from top companies. In this role, you will apply your expertise in statistics, machine learning, experimentation, data analysis, and quantitative reasoning to evaluate and improve the capabilities of advanced AI systems.
You will work on expert-level data science problems, assess AI-generated solutions, identify methodological and statistical weaknesses, and provide structured feedback that helps models reason more accurately and effectively.
Responsibilities
Evaluate AI-generated outputs across data science, statistics, machine learning, and quantitative reasoning problems.
Create expert-level prompts and datasets designed to assess advanced quantitative capabilities.
Develop accurate reference solutions for complex data science and statistical problems.
Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning.
Assess the validity and quality of AI-generated analytical approaches.
Review model outputs for technical accuracy, logical consistency, and sound statistical reasoning.
Provide structured, actionable feedback to improve AI model performance.
Apply strong quantitative judgment when evaluating experimental designs, analytical methods, and data-driven conclusions.
Communicate technical findings and statistical concepts clearly and precisely.
Required Qualifications
At least 1 year of professional experience at a top company in technology, finance, research, or a comparable environment.
At least part of the qualifying professional experience must have been within the past 7 years.
Strong expertise in data science, statistics, machine learning, or quantitative analysis.
Undergraduate degree from a top-ranked university.
Strong analytical and quantitative reasoning abilities.
Ability to evaluate complex statistical and machine-learning methodologies.
Clear written communication of technical and statistical concepts.
Ability to identify subtle errors in quantitative reasoning and analytical approaches.
Professional proficiency in English.
Preferred Qualifications
Professional experience combining Python, machine learning, statistics, and data analysis.
Experience with Pandas and other data science tools.
Experience with experimentation and experimental design.
Strong understanding of data modeling and quantitative methods.
Experience developing analytical datasets or benchmark problems.
Background in quantitative roles within technology, finance, or research organizations.
Experience evaluating technical or AI-generated outputs.
Strong ability to explain complex quantitative concepts clearly and concisely.
Compensation
$245–$280 per hour
Contractor engagement
Fully remote
Expert-level work supporting AI evaluation and quantitative reasoning
Eligibility
Candidates should have at least 1 year of experience working at a top company in technology, finance, research, or a related field.
At least part of this experience should have been obtained within the past 7 years.
Undergraduate degree from a top-ranked university.
Candidates should be based in an English-speaking country.
Strong written English and technical communication skills are required.
Application Process
Submit your application highlighting your Data Science and quantitative experience.
Detail your professional experience at relevant technology, finance, or research organizations.
Highlight your expertise in statistics, machine learning, experimentation, and quantitative analysis.
Provide information about your academic background and relevant technical skills.
Selected candidates will be considered based on their quantitative expertise, professional background, and project fit.