Data Scientist

This is a fully remote position, open to applicants in New York.

📋 Description

• Assess practical data science deliverables generated by AI systems or human contributors.

• Examine exploratory data analyses, statistical models, machine learning pipelines, technical documentation, Python, SQL, notebooks, and other related analytical outputs.

• Evaluate the accuracy of analyses, methodologies, completeness, practical applicability, and whether conclusions are substantiated by the available data.

• Identify incorrect assumptions, methodological flaws, and conclusions that lack support.

• Create specific, task-oriented grading criteria and reproducible, justifiable evaluation standards.

• Analyze feature engineering, model selection, validation, interpretation, experimentation, A/B testing, and causal inference techniques.

• Offer comprehensive written justifications for evaluation scores and relay technical findings to both specialist and general audiences.

• Integrate structured reviewer feedback and align evaluations with established standards.

• Ensure accuracy and consistency throughout complex review assignments.

• Become part of a talent network for potential future advanced AI and data science consulting projects; currently, no immediate projects are available.

⛳️ Requirements

• Minimum of 1 year of professional data science experience.

• Strong expertise in Python and SQL.

• Practical experience with statistical modeling and machine learning techniques.

• Background in designing or analyzing experiments and A/B tests.

• Familiar with causal inference methodologies.

• Ability to effectively work with messy, real-world datasets.

• Strong analytical skills and keen attention to detail.

• Excellent written communication capabilities.

• Proficiency in articulating technical conclusions clearly and accurately.

• Willingness to receive feedback and adjust professional judgment according to established standards.

• A degree in data science, statistics, computer science, mathematics, economics, engineering, or a related quantitative field may be highly relevant.

• Advanced training in quantitative analysis or machine learning may enhance an application.

• Equivalent professional experience demonstrating strong data science expertise may also be taken into account.

• Practical experience in delivering thorough real-world analysis is particularly valuable.

• Nice to have: experience in a leading technology, AI research, quantitative finance, or research organization.

• Nice to have: advanced knowledge in statistical experimentation and causal inference.

• Nice to have: familiarity with production machine learning workflows.

• Nice to have: experience in exploratory analysis across large or complex datasets.

• Nice to have: creation of technical notebooks or analytical reports.

• Nice to have: experience in reviewing or mentoring fellow data scientists.

• Nice to have: involvement in AI evaluation, structured reviews, benchmarking, or human-data projects.

🏝️ Benefits

• Opportunities for potential future remote consulting.

• Project-based roles available.

• Potential compensation rates of $95–$145 per hour depending on expertise and the specifics of each project.

• Flexible workload, duration, schedule, and responsibilities that vary by project.