Data Scientist Team Lead

What you'll do

This is a quality-lead role rather than a first-pass trainer seat. You spot-check data science items, read AI-generated explanations, Python, R and SQL snippets, modelling workflows and experiment designs, and judge them against the project rubric: statistical accuracy, data leakage, metric choice, reproducibility, and whether a library or API in the answer actually exists. Then you write the feedback, usually in direct messages, and escalate what keeps recurring.

Around the review sits the management work: telling trainers and QAs about guideline changes on Discord, answering their questions on assumptions and validation, nudging contributors who have gone quiet, running onboarding calls, and maintaining the style guides, trackers, FAQs and calibration tasks. The role is remote and paid by the hour.

Who fits

• A degree in data science, statistics, computer science, machine learning, mathematics, economics, engineering or a close quantitative field
• 3+ years in data science, analytics, ML, statistical modelling, experimentation, data engineering, technical review or data science teaching
• Solid footing in statistics, feature engineering, supervised and unsupervised methods, model evaluation and validation
• Able to catch flawed assumptions, incorrect metrics, non-reproducible code and misleading conclusions
• Strong English for guidelines and written feedback

Python, pandas, scikit-learn, SQL, Jupyter, R, Spark, Git and MLflow are listed as preferred, as is experience leading remote teams of trainers or analysts. AI training and LLM evaluation experience is called a strong plus. Applicants must be in the US.

What it pays

The ad states pay of up to $110 an hour. It gives no floor and does not say what decides where in that range an individual lands. That is the highest ceiling among the team-lead listings in this batch, which fits the technical bar. The ad does not describe payment terms; SuperAnnotate's company page covers the Deel contract, paid bootcamp and monitoring software that apply across its hourly projects.

Worth knowing

Good:

• A high stated ceiling for a remote, hourly role
• Combines technical review with team leadership, which is a step up from reviewing items alone
• Access to future projects through the expert network

Less good:

• The ad says there is no immediate project for this role; qualified candidates are among the first contacted when a relevant one opens, so this is a talent-pool listing rather than a seat with guaranteed work.
• The ad describes the selection process as an AI interview, a domain-specific task and an interview with a recruiter.
• Much of the job is coordination: chasing inactive contributors, running calls and keeping documents current, not data science itself
• The ad gives no hours, project length or start date; open to US applicants only

About this listing

Read from the SME Careers jobs board on 27 October 2026. The board shows no posting date, so we have not claimed one. Pay, location and requirements are the ad's own; the comparison to other roles in the batch is ours. See SuperAnnotate.