Data Science Team Lead for AI Training QA Remote
Overview
This remote contract role places you as a data science QA Lead guiding quality and consistency across AI training programs. You review AI-generated content and trainer outputs, applying quality standards and providing precise written feedback to uphold guidelines. You assess statistics and model evaluation to detect issues such as data leakage or non-reproducible code. You support onboarding, maintain documentation, and help activate contributors who drift from guidelines.
What You'll Do9
- 1Perform spot-checks on data science outputs to identify quality issues and share targeted feedback via Discord DMs.
- 2Conduct technical reviews of AI-generated data science explanations, Python or R snippets, modeling workflows, and metric interpretations.
- 3Update trainers and QAs on guideline changes and workflow updates to align expectations.
- 4Answer questions on statistical assumptions, metrics, validation, and rubric interpretation to guide contributors.
- 5DM inactive contributors to re-engage, track follow-ups, and flag availability issues.
- 6Create and maintain data science style guides, trackers, FAQs, examples, calibration tasks, and onboarding materials.
- 7Lead onboarding calls with contributors to explain project expectations, rubrics, and data science review standards.
- 8Flag misleading, overconfident, or non-reproducible outputs and propose corrective actions.
- 9Identify recurring quality gaps and help design scalable QA processes.
Requirements10
- 1Bachelor’s, Master’s, or PhD in data science, statistics, computer science, machine learning, mathematics, economics, engineering, or a closely related quantitative field.
- 2Strong English proficiency to follow guidelines, communicate with teams, and provide clear technical feedback.
- 33+ years of professional experience in data science, analytics, machine learning, statistical modeling, experimentation, data engineering, technical review, or data science education.
- 4Solid grasp of statistics, probability, data cleaning, exploratory data analysis, feature engineering, supervised and unsupervised learning, model evaluation, experimentation, regression, classification, clustering, and validation methods.
- 5Ability to evaluate data science content against rubrics and identify issues such as data leakage, flawed metrics, non-reproducible code, or misleading conclusions.
- 6Familiarity with Python, pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, R, Spark, Git, MLflow, notebooks, dashboards, and cloud/data platforms is preferred.
- 7Experience leading or supporting remote teams of trainers, annotators, analysts, data scientists, engineers, educators, or QAs.
- 8Comfortable using Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and project management systems.
- 9Highly organized and able to maintain style guides, trackers, onboarding materials, calibration tasks, and quality documentation.
- 10Experience with AI training, data annotation, LLM evaluation, data science QA, or rubric-based technical review is a strong plus.
Who Should Apply
Candidates with 3+ years in data science, analytics, or ML who have led or supported remote teams of trainers or analysts. They review outputs against rubrics, provide precise written feedback, and communicate guidelines clearly using Discord or collaborative docs. They are comfortable with Python, SQL, and data tooling and can explain statistical concepts in plain language. This role is less suitable for those lacking remote collaboration experience or without a track record of rubric-based quality reviews. Applicants who cannot provide concrete examples of improving training data quality or who struggle to follow project guidelines may score low.
Salary Insight
Pay is $110/hr. This hourly remote contract aligns with mid-senior level data science QA leadership in AI training programs.
Location
Required Skills
Application Tip
Highlight a concrete instance where you applied rubric-based evaluation to raise data quality, and attach a sample feedback note (Discord DM or Google Doc) that demonstrates clear, actionable guidance.
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How your application is processed
1Application received
Your resume and details are logged the moment you apply.
2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
3Employer sees qualified profiles only
Only candidates who clear screening move forward.
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