Data Scientist Team Lead

Key Responsibilities

• Spot-check data science items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• Evaluate AI-generated data science explanations, Python/R/SQL snippets, modeling workflows, statistical interpretations, dashboards, experiment designs, and step-by-step reasoning.
• Update trainers/QAs on Discord about guideline changes, workflow updates, and data-science-specific quality expectations.
• Respond to questions around statistical assumptions, metrics, model selection, data leakage, validation, coding choices, reproducibility, and rubric interpretation.
• DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Create and maintain data science style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
• Schedule and run onboarding/training calls with contributors to explain project expectations, workflows, rubrics, and data science review standards.
• Flag misleading, overconfident, statistically invalid, or non-reproducible data science outputs.
• Identify recurring quality gaps and help build scalable QA processes.

Requirements

• Bachelor’s, Master’s, or PhD degree in Data Science, Statistics, Computer Science, Machine Learning, Mathematics, Economics, Engineering, or a closely related quantitative field.
• Strong grasp of English to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of professional experience in data science, analytics, machine learning, statistical modeling, experimentation, data engineering, technical review, or data science education.
• Strong understanding of statistics, probability, data cleaning, exploratory data analysis, feature engineering, supervised/unsupervised learning, model evaluation, experimentation, regression, classification, clustering, and validation methods.
• Ability to evaluate data science content against detailed rubrics and identify issues such as data leakage, flawed assumptions, incorrect metrics, weak methodology, non-reproducible code, hallucinated libraries/APIs, or misleading conclusions.
• Familiarity with tools such as Python, pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, R, Spark, Git, MLflow, notebooks, dashboards, and cloud/data platforms is preferred.
• Experience leading or supporting remote teams of trainers, annotators, analysts, data scientists, engineers, educators, or QAs is strongly preferred.
• Comfortable using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
• Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentation.
• Experience with AI training, data annotation, LLM evaluation, data science QA, or rubric-based technical review is a strong plus.