Quantitative Statistics Expert - Freelance AI Trainer at Mindrift

, , Ireland -

Full Time

Start Date

Immediate

Expiry Date

30 Mar, 26

Salary

50.0

Posted On

30 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Statistical Modeling, Data Analysis, Descriptive Statistics, Inferential Statistics, Regression Analysis, Experimental Design, Statistical Learning Methods, Problem Design, Evaluation Tasks, Conceptual Understanding, Rigorous Reasoning, Statistical Interpretation, Statistical Guidelines, Judgment, Learning New Methods, Complex Tasks

Industry

technology;Information and Internet

Description

This opportunity is only for candidates currently residing in the specified country. Your location may affect eligibility and rates. Please submit your resume in English and indicate your level of English. At Mindrift, innovation meets opportunity. We believe in using the power of collective intelligence to ethically shape the future of AI. What we do The Mindrift platform connects specialists with AI projects from major tech innovators. Our mission is to unlock the potential of Generative AI by tapping into real-world expertise from across the globe. About the Role GenAI models are improving very quickly, and one of our goals is to make them capable of addressing specialized questions and achieving complex reasoning skills. If you join the platform as an AI Tutor in Statistics, you’ll have the opportunity to collaborate on these projects. Although every project is unique, you might typically: Generate prompts that challenge AI. Define comprehensive scoring criteria to evaluate the accuracy of the AI’s answers. Correct the model’s responses based on your domain-specific knowledge. How to get started Simply apply to this post, qualify, and get the chance to contribute to projects aligned with your skills, on your own schedule. From creating training prompts to refining model responses, you’ll help shape the future of AI while ensuring technology benefits everyone. Effort estimate Tasks for this project are estimated to take 6–10 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted. Bachelor’s Degree in Statistics, Biostatistics, Econometrics, or a closely related quantitative field. At least 2+ years of relevant professional experience in statistics or statistical modeling. Level of English is advanced (C1) or above. Strong expertise in statistical modeling and data analysis, including descriptive and inferential statistics, regression analysis, experimental design, and statistical learning methods. Ability to design high-quality, diverse statistical problems and evaluation tasks, going beyond basic parameter changes and requiring conceptual understanding, rigorous reasoning, and correct statistical interpretation. Experience working with complex or ambiguous statistical guidelines, with the ability to interpret requirements accurately and apply consistent judgment. Willingness to learn new statistical methods, switch efficiently between topics, and handle intellectually challenging tasks. This is a fully remote freelance role — you only need a laptop, a stable internet connection, sufficient availability, and motivation to take on challenging statistical work. Why this freelance opportunity might be a great fit for you? Get paid for your expertise, with rates that can go up to $50/hour depending on your skills, experience, and project needs. Take part in a part-time, remote, freelance project that fits around your primary professional or academic commitments. Work on advanced AI projects and gain valuable experience that enhances your portfolio. Influence how future AI models understand and communicate in your field of expertise.

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Responsibilities

As an AI Tutor in Statistics, you will generate prompts that challenge AI and define scoring criteria to evaluate the accuracy of AI responses. You will also correct the model’s responses based on your domain-specific knowledge.