This role is for one of our clients
Pay: 150–200/hour
Role Type: Contractor
Location: Remote
We are seeking a detail-oriented Junior Data Scientist to contribute to the evaluation and improvement of advanced artificial intelligence systems. This opportunity is ideal for early-career data science professionals with strong analytical skills and a solid understanding of statistics, machine learning, experimentation, and quantitative reasoning.
In this role, you will evaluate AI-generated responses to data science and quantitative problems, develop expert-level prompts and datasets, and create reference solutions to assess the accuracy and reliability of AI models. You will identify statistical errors, flawed methodologies, and weaknesses in quantitative reasoning while providing structured feedback to improve model performance.
The ideal candidate combines practical data science knowledge with clear technical communication and a rigorous, evidence-based approach to problem-solving. Previous experience in AI development is not necessarily required.
Requirements
Key Responsibilities
- AI Model Evaluation: Evaluate AI-generated outputs on data science, statistics, machine learning, experimentation, and quantitative analysis tasks.
- Problem and Prompt Development: Create challenging, well-defined prompts and technical problems that assess analytical reasoning and practical data science skills.
- Dataset Preparation: Prepare and validate relevant datasets for testing AI models across statistical and machine learning use cases.
- Reference Solution Development: Develop accurate reference answers, calculations, and analytical solutions to establish reliable evaluation benchmarks.
- Statistical Validation: Identify statistical errors, incorrect assumptions, flawed methodologies, and inappropriate interpretations of data.
- Quantitative Reasoning Assessment: Assess whether AI-generated solutions demonstrate sound mathematical reasoning, appropriate analytical methods, and evidence-based conclusions.
- Structured Feedback: Provide clear, actionable feedback highlighting errors, missing steps, and opportunities to improve the accuracy and reliability of AI-generated outputs.
- Quality Assurance: Validate analytical results, review solution consistency, and ensure that evaluation criteria are objective and reproducible.
- Technical Documentation: Explain statistical concepts, analytical approaches, and evaluation findings in a clear and structured manner.
Required Qualifications
- At least one year of professional experience at a well-established company in technology, finance, research, or a related field.
- At least part of the relevant professional experience should have been gained within the past three years.
- Foundational knowledge of data science, statistics, machine learning, and quantitative analysis.
- Strong analytical and problem-solving skills, with the ability to evaluate data-driven conclusions critically.
- Understanding of statistical methods, experimental design, data interpretation, and model evaluation.
- Ability to identify incorrect calculations, methodological weaknesses, and unsupported conclusions.
- Strong written communication skills, particularly when explaining technical and statistical concepts.
- Ability to work independently, follow evaluation guidelines, and deliver accurate, high-quality assessments.
Must-Have Skills
- Data Science Fundamentals
- Statistics and Probability
- Machine Learning Fundamentals
- Quantitative Reasoning
- Data Analysis and Interpretation
- Statistical Methodology
- Analytical Problem-Solving
- Technical Writing and Documentation
Good-to-Have Skills
- Python Programming
- SQL and Data Manipulation
- Pandas, NumPy, and scikit-learn
- Experimental Design and Hypothesis Testing
- Predictive Modeling and Model Evaluation
- Dataset Creation and Validation
- AI Model Evaluation and Benchmarking
- Experience in Technology, Finance, or Research Organizations
Preferred Qualifications
- Experience working at a recognized technology company, financial institution, or research organization.
- Professional exposure to statistical analysis, machine learning projects, quantitative research, or data-driven decision-making.
- Familiarity with evaluating AI-generated answers or validating analytical solutions.
- Ability to communicate complex quantitative concepts in clear, concise English.
- Candidates based in English-speaking countries are preferred.