Data Scientist Team Lead

Skills

About this role

In this hourly, remote contractor role, you will work as a Data Scientist Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across data science AI training projects. You will review AI-generated data science content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards.

Data Science, Statistics, Machine Learning

Responsibilities

• Quality monitoring: Spot-check data science items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issues.
• Technical review: Evaluate AI-generated data science explanations, Python/R/SQL snippets, modeling workflows, statistical interpretations, dashboards, experiment designs, and step-by-step reasoning.
• Trainer and QA communication: Update trainers/QAs on Discord about guideline changes, workflow updates, and data-science-specific quality expectations.
• Question handling: Respond to questions around statistical assumptions, metrics, model selection, data leakage, and validation.
• Trainer/QA activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.

Qualifications

• 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, or non-reproducible code.