Online Statistics Graduate Level Tutor

Job Description

Online Statistics Graduate Level Tutor | Varsity Tutors

The Tone:
This is a contract role at Varsity Tutors, located remotely. Varsity Tutors operates a Live Learning Platform, leveraging advanced technology, AI, and the latest in learning science to create personalized learning experiences. This role is crucial for connecting graduate-level statistics students with expert instruction, directly impacting their academic success and deepening their understanding from the convenience of your home.

The TL;DR
• Role: Contract
• Location: Remote
• Pay: $40 hourly
• Mission: Provide personalized, expert instruction in graduate-level statistics to help students achieve academic success.
• Tech Stack: R, Python

What You’ll Actually Do
• Deliver personalized 1-on-1 online tutoring sessions and accept on-demand requests for graduate-level students.
• Clarify advanced statistical theories, derivations, and complex model outputs using multiple approaches.
• Guide students through designing experiments, performing multivariate analysis, constructing confidence regions, and implementing statistical models in software.
• Prepare masters and doctoral students for advanced research methodology and statistical analysis.
• Adapt instruction based on individual learning needs and styles, utilizing research paper examples and proof-based exercises.

The Must-Haves
• Background: Advanced subject mastery in statistics, encompassing deep knowledge of mathematical statistics, hypothesis testing theory, Bayesian inference, regression analysis, multivariate methods, and experimental design.
• Experience: Proven ability to break down complex derivations and calculations, guide students through designing experiments, and implement statistical models. Familiarity with graduate statistics curricula and common academic challenges.
• Skills: Expertise in maximum likelihood estimation, sufficient statistics, nonparametric statistics, asymptotic theory, Neyman-Pearson lemma, and generalized linear models. Proficiency in likelihood function derivations, posterior distribution calculations, and ANOVA decompositions. Strong communication skills and an engaging teaching style.