**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.

**Key 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.