Online Statistics Graduate Level Tutor

Job Description

Online Statistics Graduate Level Tutor | Varsity Tutors

The Tone:
This is a freelance, remote position at Varsity Tutors. The company operates a live learning platform that leverages advanced technology, AI, and learning science to transform how people learn. This role is essential for providing personalized graduate-level statistics instruction, enabling students to achieve academic success and understanding through customized learning experiences. Tutors make a significant impact on student outcomes from the comfort of their home.

The TL;DR
• Role: Freelance
• Location: Remote
• Pay: $40 hourly
• Mission: To provide personalized graduate-level statistics instruction that empowers students to achieve their learning goals.
• Tech Stack: R, Python, AI-powered Tutor Copilot, Live Learning Platform

What You’ll Actually Do
• Instruct: Provide personalized, one-on-one online instruction to individual students tailored to their specific needs.
• Simplify: Break down complex concepts such as likelihood function derivations, posterior distribution calculations, and ANOVA decompositions.
• Guide: Guide students through designing experiments, performing multivariate analysis, constructing confidence regions, and implementing statistical models in software.
• Adapt: Adapt instruction using R or Python statistical computing, research paper examples, and proof-based exercises to support masters and doctoral students.
• Explain: Identify concepts students commonly struggle with and explain advanced material using multiple approaches.

The Must-Haves
• Background: Advanced academic background with deep knowledge of mathematical statistics, maximum likelihood estimation, hypothesis testing theory, Bayesian inference, regression analysis, multivariate methods, experimental design, and nonparametric statistics. Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models, preparing students for research methodology and doctoral-level statistical analysis.
• Experience: Proven ability to skillfully break down and explain complex concepts such as likelihood function derivations and ANOVA decompositions. Experience guiding students through designing experiments, performing multivariate analysis, and implementing statistical models in software. Familiarity with graduate statistics curricula, including measure-theoretic probability foundations and deriving estimator properties.
• Skills: Strong communication skills with a friendly and engaging teaching style. Adeptness at conceptual teaching, problem-solving, and adapting instruction to meet individual learning needs and styles. Proficiency in R or Python statistical computing.