[Remote] Data Scientist Quality Assurance Lead (QAL)

Note: The job is a remote job and is open to candidates in USA. SME Careers is a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. They are seeking a Data Scientist Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across data science AI training projects, ensuring that training data is analytically sound and aligned with client expectations.ResponsibilitiesQuality monitoring: Spot-check data science items, identify quality issues, provide feedback through DMs, and escalate recurring or critical issuesTechnical review: Evaluate AI-generated data science explanations, Python/R/SQL snippets, modeling workflows, statistical interpretations, dashboards, experiment designs, and step-by-step reasoningTrainer and QA communication: Update trainers/QAs on Discord about guideline changes, workflow updates, and data-science-specific quality expectationsQuestion handling: Respond to questions around statistical assumptions, metrics, model selection, data leakage, validation, coding choices, reproducibility, and rubric interpretationTrainer/QA activation management: DM inactive contributors, encourage activation, track follow-ups, and flag availability issuesDocumentation: Create and maintain data science style guides, trackers, FAQs, examples, honeypots, calibration tasks, and onboarding materialsOnboarding and training: Schedule and run onboarding/training calls with contributors to explain project expectations, workflows, rubrics, and data science review standardsRisk review: Flag misleading, overconfident, statistically invalid, or non-reproducible data science outputsProcess improvement: Identify recurring quality gaps and help build scalable QA processesSkillsBachelor's, Master's, or PhD degree in Data Science, Statistics, Computer Science, Machine Learning, Mathematics, Economics, Engineering, or a closely related quantitative fieldStrong grasp of English to follow guidelines, communicate with teams, and provide clear technical feedback3+ years of professional experience in data science, analytics, machine learning, statistical modeling, experimentation, data engineering, technical review, or data science educationStrong understanding of statistics, probability, data cleaning, exploratory data analysis, feature engineering, supervised/unsupervised learning, model evaluation, experimentation, regression, classification, clustering, and validation methodsAbility 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 conclusionsComfortable using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systemsHighly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentationFamiliarity with tools such as Python, pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, R, Spark, Git, MLflow, notebooks, dashboards, and cloud/data platformsExperience leading or supporting remote teams of trainers, annotators, analysts, data scientists, engineers, educators, or QAsExperience with AI training, data annotation, LLM evaluation, data science QA, or rubric-based technical reviewCompany OverviewSME Careers by SuperAnnotate connects subject-matter experts, students, and professionals with flexible, remote AI training work such as annotation, evaluation, fact-checking, and content review. It was founded in undefined, and is headquartered in San Francisco, California, US, with a workforce of 11-50 employees. Its website is https://sme.careers/.