Data Scientist Quality Assurance Lead (Remote)

About the role

This contract position is focused on leading quality assurance for AI‑generated data‑science training content. You will work remotely, reviewing outputs, providing precise feedback, and ensuring that all deliverables meet rigorous statistical and methodological standards.

Key responsibilities

• Oversee quality, consistency, and trainer performance across data‑science AI training projects.
• Review AI‑generated content and trainer/QA work against detailed rubrics.
• Evaluate statistical accuracy, data reasoning, model‑selection quality, code correctness, reproducibility, metric interpretation, and business‑context relevance.
• Provide clear written feedback and ensure contributors follow quality standards.
• Identify recurring quality issues, communicate updates to trainers and QAs, and support onboarding of new contributors.
• Maintain documentation and help reactivate contributors who are not working consistently.

Required profile

• Bachelor’s, Master’s or PhD in Data Science, Statistics, Computer Science, Machine Learning, Mathematics, Economics, Engineering or a related quantitative field.
• Strong command of English for guideline interpretation and feedback communication.
• 3+ years of professional experience in data science, analytics, machine learning, statistical modeling, experimentation, data engineering, technical review or data‑science education.
• Deep understanding of statistics, probability, data cleaning, exploratory analysis, feature engineering, supervised/unsupervised learning, model evaluation, regression, classification, clustering and validation methods.
• Proven ability to evaluate data‑science content against detailed rubrics and spot issues such as data leakage, flawed assumptions, incorrect metrics or non‑reproducible code.
• Experience leading or supporting remote teams of trainers, annotators, analysts, data scientists, engineers or QAs is strongly preferred.

Required skills

• Python
• Pandas
• NumPy
• scikit‑learn
• SQL
• Jupyter notebooks
• Matplotlib
• R
• Apache Spark
• Git
• MLflow