The Statistics Tutor will support xAI’s AI tutoring initiative by providing high‑quality statistics annotations, selecting and developing challenging problems, and collaborating with technical staff to improve training data and annotation tools. The role is remote and may be full‑time, part‑time, or contractor. Candidates should hold a Master’s or PhD in Statistics or a related field, have strong analytical and communication skills, and may have published research or teaching experience.
Statistics Tutor at xAI
Remote - Remote
More jobs at xAISalary
R$ 45 - 75
Requirements
Skills
- Master’s or PhD in Statistics (or Mathematics with a specialization in statistics or probability)
- Medal in an International Math Olympiad (IMO), International Statistical Olympiad, or similar competition
- Proficiency in reading and writing both in informal and professional English
- Strong ability to navigate various information resources and databases
- Outstanding communication, interpersonal, analytical, and organizational capabilities
- Solid reading comprehension skills combined with capacity to exercise autonomous judgment
- Strong passion for and commitment to technological advancements and innovation
- PhD in Statistics or a closely related quantitative field
- Peer-reviewed publications in statistics, probability, or applied statistical modeling
- Previous AI tutoring experience and/or experience teaching college-level statistics courses
Responsibilities
- Use proprietary software applications to provide input/labels on defined projects
- Support and ensure the delivery of high-quality curated data
- Play a pivotal role in supporting and contributing to the training of new tasks and the development of cutting‑edge initiatives/technologies
- Interact with the technical staff to help improve the design of efficient annotation tools
- Choose problems from statistical domains that align with your expertise, focusing on areas such as probability theory, inferential statistics, regression modeling, multivariate analysis, stochastic processes, Bayesian methods, experimental design, and data‑driven applications
- Interpret, analyze, and execute tasks based on given instructions
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