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Research Data Scientist - AI Trainer

DataAnnotation

New Zealand · 정규직

가장 먼저 지원하세요

경험
2년 이상
샐러리
USD 50 – USD 100 / hour
채용 공고
1
게시됨
3주 전
작업 모드
사무실에서
교육
Bachelor's degree in a quantitative field preferred; master's or PhD is a plus
적임
Applicants must be based in the United States, Canada, the United Kingdom, Ireland, Australia, or New Zealand. The role is open to experienced quantitative professionals from data science, statistics, economics, finance, physics, biology, epidemiology, operations research, and similar fields.
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직무 설명

About the role

This position is aimed at experienced quantitative professionals who want to help improve AI systems. The work involves assessing AI-generated analysis, solving complex technical challenges, and sharing feedback that helps refine how models reason through data, modeling, and scientific questions.

The role can suit people from a range of quantitative backgrounds, including data science, astrophysics, economics, biostatistics, operations research, and other closely related fields. If you are comfortable thinking critically about data and mathematical or statistical models, your background may be a strong fit. Some contributors combine this with another full-time job, while others make it their main focus.

How the process works

After creating an account, you will complete a short assessment that acts as the screening step. If successful, you will receive confirmation by email and paid projects will become available through the platform.

Contract benefits

  • You can select the projects you want to take on and decide when to work.
  • The work is done on your own schedule, using your own computer, from home.
  • Pay starts at USD 50 to USD 100+ per hour, and some projects may include additional bonus pay.

Responsibilities

  • Review AI-produced quantitative outputs and check them for technical correctness and practical validity, including statistical analysis, predictive modeling, scientific reasoning, and data-driven conclusions.
  • Build and solve quantitative tasks used to train and evaluate AI systems, covering areas such as forecasting, experimental analysis, optimization, and statistical inference.
  • Produce clear technical write-ups and maintain well-structured analytical code.
  • Share feedback that influences the development of future AI models designed for quantitative reasoning.

Qualifications

  • At least 2 years of practical experience in a quantitative role or research setting, such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or a related domain.
  • Basic coding ability, with the confidence to write and review analytical code from start to finish.
  • Hands-on familiarity with statistical techniques, predictive modeling, and experiment design, including A/B testing, hypothesis testing, regression, classification, and time-series forecasting.
  • Strong English fluency at a native or bilingual level, along with excellent written communication.
  • A bachelor's degree in a quantitative discipline such as Statistics, Computer Science, Mathematics, Engineering, or a similar subject is preferred; a master's degree or PhD is an added advantage.
  • Additional proof of expertise, such as a strong Kaggle ranking, AWS or GCP machine learning certification, or similar credentials, is considered a plus.

Eligibility

This opportunity is open only to applicants based in the United States, Canada, the United Kingdom, Ireland, Australia, or New Zealand. The role is structured as an independent contractor engagement, and payment is processed through PayPal. Currency conversion from USD is handled by PayPal. No payment or fee is ever requested from applicants.

Additional information

This opportunity is paid on an hourly basis, but no fixed salary, stipend, or total compensation figure has been provided. The work arrangement is described as an independent contractor arrangement. The role is expected to be carried out on-site according to the source data, though the job details also describe working from home using your own computer.

Tags

#datascience

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