Applied Scientist

$245–280/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: Worldwide
Field: Sciences

Apply statistics, experimental design, and machine learning to evaluate and improve AI systems. The work includes producing reproducible analyses, reviewing quantitative reasoning, and writing expert prompts, datasets, and reference solutions.

The work

This remote contractor role combines applied data science with AI evaluation. Day to day, you may design statistical analyses, build and validate machine learning models, transform structured or unstructured data, define experimentation frameworks, and document findings in reproducible code and analytical reports.

AI evaluation responsibilities

You will assess model responses involving statistics, machine learning, experimentation, and other quantitative problems. That includes creating expert-level prompts and reference solutions, detecting methodological or statistical errors, reviewing peer contributions, and writing feedback precise enough to improve future model behavior. The platform is likely to screen for verifiable project experience, sound choices about metrics and validation, and the ability to explain technical judgments under follow-up.

Qualifications and logistics

Python expertise, including Pandas and scikit-learn (sklearn), is preferred. The listing also prefers practical experience in applied statistics, experimental design, machine learning, data modeling, and technical documentation, while experience at a top technology, finance, or research company and an advanced academic background are advantageous rather than stated requirements. No prior AI experience is required.

The engagement is remote and structured as contract work. The listing does not specify a weekly schedule, typical hours, contract duration, or whether work is fully asynchronous, so candidates should confirm those details during onboarding.

Pay band: $245–280/hr

What they look for

Python
Machine Learning
Statistics
Data Analysis
Experimentation
Pandas
Data Modeling

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether you can connect a verifiable dataset, analytical method, and measured result to a real decision rather than discussing modeling in the abstract.
A strong answer: Choose one project and state the data source, target or hypothesis, method, validation approach, and resulting decision. Distinguish your own contribution from the work of the broader team.
Where people lose it: Candidates lose points by naming sophisticated methods without explaining why the analysis was appropriate or what decision changed because of it.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.