Data Scientist QA Lead - Remote

Job Description

Job Title: Data Scientist Quality Assurance Lead

Job Type: Contract

Location: Remote

About This Role

In this hourly, remote contractor role, you will work as a Data Scientist Quality Assurance Lead 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. You will assess work for statistical accuracy, data reasoning, model-selection quality, code correctness, reproducibility, metric interpretation, business-context awareness, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role is a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. Your data science quality leadership will help ensure training data is analytically sound, reproducible, clearly explained, and aligned with client expectations. Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter. Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Your Profile

• 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.
  
  

• 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, validation, coding choices, reproducibility, and rubric interpretation.
• Trainer/QA activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Documentation: Create and maintain data science style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materials.
• Onboarding and training: Schedule and run onboarding/training calls with contributors to explain project expectations, workflows, rubrics, and data science review standards.
• Risk review: Flag misleading, overconfident, statistically invalid, or non-reproducible data science outputs.
• Process improvement: Identify recurring quality gaps and help build scalable QA processes.