Create expert-level prompts, datasets, and reference solutions for model evaluation.
Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning.
Produce reproducible code, analytical reports, and clear technical documentation.
Review peer contributions and provide constructive feedback to support quality and knowledge sharing.
Document methodologies, findings, and recommendations with precision.
Required Qualifications
Strong Python programming skills, including proficiency with Pandas and familiarity with scikit-learn.
Demonstrated expertise in applied statistics, experimental design, and machine learning.
Experience developing, tuning, validating, and evaluating machine learning models.
Strong data modeling skills and experience managing data transformations.
Ability to identify statistical errors, methodological flaws, and weaknesses in analytical reasoning.
Excellent written and verbal communication skills.
Ability to produce reproducible code and well-organized technical reports.
Preferred Qualifications
At least one year of experience at a leading technology, finance, or research company.
Advanced academic training or practical experience in data science, applied mathematics, or a related field.
Experience working on cross-functional or collaborative data science projects.
Strong technical documentation and analytical review skills.
Key Skills
Applied science, Python, machine learning, applied statistics, data analysis, experimentation, experimental design, Pandas, scikit-learn, data modeling, statistical inference, model evaluation, quantitative reasoning, data transformation, reproducible research, analytical reporting, and technical communication.
Compensation
The hourly rate is $245–$280, equivalent to approximately $470,400–$537,600 per year at 1,920 working hours annually. This annualized figure is for comparison only; actual earnings depend on contracted hours and workload.
What to Expect
You will combine applied scientific analysis with technical evaluation of AI outputs. Typical deliverables include statistical analyses, machine learning solutions, reproducible code, expert reference datasets, and structured feedback on quantitative reasoning. The work emphasizes methodological rigor, analytical accuracy, and clear technical communication. Previous AI experience is not required.
