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. In this role, you will oversee quality assurance for data science AI training projects, ensuring content accuracy and adherence to project guidelines.
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, 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
Skills
- 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
- Highly organized and able to maintain style guides, trackers, FAQs, onboarding materials, honeypots, calibration tasks, and quality documentation
- Familiarity with tools such as Python, pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, R, Spark, Git, MLflow, notebooks, dashboards, and cloud/data platforms
- Experience leading or supporting remote teams of trainers, annotators, analysts, data scientists, engineers, educators, or QAs
- Comfortable using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems
- Experience with AI training, data annotation, LLM evaluation, data science QA, or rubric-based technical review
Benefits
- Access to future projects available through our expert network
Company Overview