Data Scientist QA Lead - Remote
YO IT Consulting · Dallas, TX
- Hybrid
- Full-time
- $120,000 / year
- Dallas, TX
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Job highlights
- Lead quality assurance for AI data science projects.
- Review AI-generated content and trainer performance.
- Evaluate work against detailed project guidelines.
- Provide precise feedback to improve data quality.
- Contribute to process improvement and documentation.
About the role
Data Scientist Quality Assurance Lead
YO IT Consulting is seeking a Data Scientist Quality Assurance Lead for a remote, contract role. This position is ideal for an experienced professional looking to ensure the quality and consistency of AI-generated data science content and training projects. You will play a critical role in a fast-growing AI Data Services company, contributing to the development of high-quality training data for leading AI companies.
About This Role
As a Data Scientist Quality Assurance Lead, you will oversee quality, consistency, and trainer performance across data science AI training projects. Your responsibilities include reviewing AI-generated data science content and trainer/QA work, evaluating output quality against project guidelines, providing precise written feedback, and ensuring contributors adhere to 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.
Key activities include spotting recurring quality issues, communicating updates to trainers and QAs, supporting onboarding, maintaining documentation, and helping to activate contributors who are not working consistently. Your leadership will ensure training data is analytically sound, reproducible, clearly explained, and aligned with client expectations.
The selection process involves an AI interview, a domain-specific task, and an interview with a recruiter. This role offers access to future projects through our expert network, even if there is no immediate project assignment.
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 for effective communication and 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 with experience in maintaining 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.
Key skills/competency
- Data Science
- Quality Assurance
- Machine Learning
- Statistical Modeling
- Python
- SQL
- Team Leadership
- Technical Review
- Rubric Development
- AI Training Data
Skills & topics
- Data Science
- Quality Assurance
- Machine Learning
- AI
- Lead
- Remote
- Contract
- Python
- Statistics
- Data Analysis
How to get hired
- Tailor your resume: Highlight your 3+ years of data science, QA, and leadership experience. Emphasize your quantitative background and familiarity with Python, SQL, and ML tools.
- Showcase technical skills: Detail your expertise in statistics, model evaluation, data cleaning, and identifying issues like data leakage and non-reproducible code.
- Demonstrate leadership: Provide examples of leading or supporting remote teams and your ability to communicate technical feedback clearly.
- Prepare for the process: Be ready for an AI interview, a domain-specific task, and a recruiter interview to assess your qualifications.
- Network for opportunities: Apply to join the expert network for access to future relevant projects.
Technical preparation
Master Python libraries: pandas, NumPy, scikit-learn.,Practice SQL queries for data extraction.,Review statistical concepts and model evaluation.,Familiarize with Jupyter, Git, and MLflow.
Behavioral questions
Describe a time you improved quality.,How do you give constructive feedback?,How do you handle remote team challenges?,How do you ensure accuracy and detail?
Frequently asked questions
- What is the primary responsibility of a Data Scientist Quality Assurance Lead at YO IT Consulting?
- The primary responsibility is to oversee the quality, consistency, and trainer performance for data science AI training projects. This involves reviewing AI-generated content and trainer work, ensuring adherence to project guidelines and quality standards.
- What educational background is required for the Data Scientist Quality Assurance Lead role?
- A Bachelor’s, Master’s, or PhD degree in a quantitative field such as Data Science, Statistics, Computer Science, Machine Learning, Mathematics, Economics, or Engineering is required.
- How much professional experience is needed for this position?
- You need at least 3 years of professional experience in data science, analytics, machine learning, statistical modeling, experimentation, data engineering, technical review, or data science education.
- What technical skills and tools are preferred for this role?
- Familiarity with Python, pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, R, Spark, Git, MLflow, notebooks, dashboards, and cloud/data platforms is preferred.
- Is experience with remote team leadership necessary for the Data Scientist Quality Assurance Lead job?
- Experience leading or supporting remote teams of trainers, annotators, analysts, data scientists, engineers, educators, or QAs is strongly preferred, but not strictly required.
- What is the selection process for this Data Scientist QA Lead role?
- The selection process includes an AI interview, a domain-specific task, and a final interview with a recruiter.
- Is there an immediate project for this role, or is it part of an expert network?
- There is no immediate project for this role. However, qualified candidates will be added to an expert network and contacted for future relevant opportunities.
- What specific data science concepts must a candidate understand for this QA Lead position?
- Candidates must have a strong understanding of statistics, probability, data cleaning, exploratory data analysis, feature engineering, supervised/unsupervised learning, model evaluation, experimentation, regression, classification, clustering, and validation methods.