[Hiring] Junior Data Scientist @Weekday AI

Role Description

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.

• Evaluate AI-generated responses to data science and quantitative problems.
• Develop expert-level prompts and datasets.
• Create reference solutions to assess the accuracy and reliability of AI models.
• Identify statistical errors, flawed methodologies, and weaknesses in quantitative reasoning.
• Provide structured feedback to improve model performance.

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.

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.