Junior Data Scientist – AI Model Evaluation
Compensation
Annualized Compensation: $288,000–$384,000 per year
Hourly Rate: $150–$200 per hour
Employment Type: Contractor
Work Arrangement: Remote
The annualized figure is based on 40 hours per week and 48 working weeks per year. Actual earnings will depend on project workload, assignments, and hours worked.
About the Role
We are seeking data scientists and quantitative professionals with experience at leading organizations to evaluate and improve how AI models handle statistics, machine learning, experimentation, and quantitative reasoning.
In this role, you will assess AI-generated responses to data science and quantitative problems, develop expert-level prompts and reference solutions, and identify methodological weaknesses, statistical errors, and flawed reasoning.
Your expertise will help strengthen AI model performance by ensuring that outputs demonstrate sound analytical methods, statistical rigor, and reliable quantitative reasoning.
Key Responsibilities
Evaluate AI model outputs across data science, statistics, machine learning, and quantitative problem-solving tasks.
Create expert-level prompts that test analytical ability, statistical understanding, and machine learning knowledge.
Develop datasets and reference solutions for evaluating model performance.
Identify flawed methodologies, statistical errors, and weaknesses in quantitative reasoning.
Assess the accuracy, validity, and rigor of AI-generated analytical solutions.
Provide structured feedback highlighting errors, limitations, and opportunities for improvement.
Communicate technical findings and statistical concepts clearly to support model development.
Contribute to improving AI model capabilities in data science and quantitative analysis.
Required Qualifications
At least one year of professional experience at a leading company in technology, finance, or research, with at least part of this experience gained within the past three years.
Strong understanding of statistics, machine learning, and quantitative reasoning.
Proficiency in Python for data analysis and quantitative problem-solving.
Familiarity with data analysis, experimentation, and data modeling.
Knowledge of relevant data science tools and libraries, including pandas.
Ability to identify statistical errors, evaluate methodologies, and assess analytical conclusions.
Strong written communication skills, including the ability to explain technical and statistical concepts clearly.
Based in an English-speaking country.
Preferred Qualifications
Experience evaluating statistical analyses, machine learning approaches, or quantitative solutions.
Experience designing experiments and interpreting analytical results.
Familiarity with developing datasets, technical prompts, and reference solutions.
Experience identifying methodological weaknesses and validating quantitative conclusions.
Ability to provide structured, actionable feedback on complex data science problems.
Key Skills
Data Science
Python
Machine Learning
Statistics
Data Analysis
Experimentation
Pandas
Data Modeling
Quantitative Reasoning
Statistical Analysis
Methodology Evaluation
Model Output Evaluation
Prompt Development
Dataset Development
Reference Solution Development
Analytical Problem Solving
Critical Thinking
Technical Writing
Statistical Communication
What Success Looks Like
AI-generated solutions are evaluated for statistical accuracy, methodological soundness, and quantitative rigor.
Expert-level prompts, datasets, and reference solutions effectively assess data science capabilities.
Statistical errors and weaknesses in quantitative reasoning are identified and clearly documented.
Feedback provides actionable guidance for improving AI model performance.
Evaluations contribute to stronger AI capabilities in statistics, machine learning, experimentation, and data science.