Data Scientist Team Lead

SME Careers; United States; Remote; USD 55,000 - 110,000; Full time

Job summary

SME Careers is seeking a Data Scientist Quality Assurance Lead (QAL) for an hourly, remote contractor role to oversee quality across AI training projects. You will review AI-generated data science content, assess trainer output, and provide precise written feedback to ensure adherence to project guidelines.

You will lead technical reviews of Python/R/SQL snippets, scoring rubrics, and dashboards, while coordinating with remote trainers and QAs. Selection includes AI interview and domain tasks.

Qualifications

• Degree in Data Science, Statistics, CS, ML, Math, Economics, Engineering, or related field.
• Strong English proficiency for guideline following and technical feedback.
• 3+ years of professional experience in data science or related roles.
• Solid knowledge of statistics, data cleaning, feature engineering, and model evaluation.

Responsibilities

• Quality monitoring of data science items and provide feedback.
• Conduct technical reviews of AI-generated explanations and code snippets.
• Communicate guideline changes to trainers and QAs via Discord.
• Help onboarding and documentation for QA processes and style guides.
• Identify recurring quality gaps and drive process improvements.

Skills

Quality assurance leadership

Data science expertise

Technical feedback writing

Remote team collaboration

Problem solving

Education

Bachelor's/Master's/PhD in Data Science or related quantitative field

Tools

Python

Pandas

NumPy

scikit-learn

SQL

Jupyter

R

Spark

Git

MLflow

Dashboards

Cloud platforms

Job description

In this hourly, remote contractor role, you will work as a Data Scientist Quality Assurance Lead (QAL) 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 with SME Careers, 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. 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.

Responsibilities

• Quality monitoring: Spot-check data science items, identify quality issues, provide feedback through DMs, and elevate 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.

Requirements

• 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.