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Junior Data Scientist

Weekday
Remote, IND
Contract
Entry · 3+ years experience
Remote
Discovered 2 days ago
numpypandaspythonscikit-learnsql

Full Job Posting

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.

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.

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.

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