Statistics Graduate Level Tutor

Job Description

Statistics Graduate Level Tutor | Varsity Tutors

The Tone:
This is a freelance position at Varsity Tutors, located remotely. Varsity Tutors operates a Live Learning Platform, leveraging advanced technology and AI to create personalized learning experiences. The role is crucial for transforming the way people learn, as tutors make a significant impact on students’ academic success and understanding through customized, one-on-one instruction.

The TL;DR
• Role: Freelance
• Type: Flexible Schedule
• Location: Remote
• Pay: $40 hourly
• Mission: To provide customized instruction that helps students achieve their learning goals in statistics.
• Tech Stack: R, Python, Live Learning Platform, AI-powered Tutor Copilot

What You’ll Actually Do
• Tutor: Provide personalized 1-on-1 online instruction to individual graduate-level statistics students.
• Guide: Guide students through complex statistical concepts, including likelihood function derivations, posterior distribution calculations, and ANOVA decompositions.
• Prepare: Prepare students for research methodology and doctoral-level statistical analysis, encompassing experimental design, multivariate analysis, and model implementation in software.
• Adapt: Adapt instructional methods using R or Python statistical computing, research paper examples, and proof-based exercises to support masters and doctoral students.
• Manage: Set your own flexible schedule, accept instant tutoring requests, and invoice for completed sessions, with all payment logistics handled by Varsity Tutors.

The Must-Haves
• Background: Advanced subject mastery in Statistics, with deep knowledge of mathematical statistics, maximum likelihood estimation, hypothesis testing theory, Bayesian inference, regression analysis, multivariate methods, experimental design, and nonparametric statistics.
• Experience: Proven ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models, emphasizing theoretical foundations and their connection to biostatistics, econometrics, and machine learning research applications.
• Skills: Strong conceptual teaching and problem-solving abilities, skilled at breaking down complex topics and identifying common student struggles. Excellent communication skills and a friendly, engaging teaching style, with the capacity to adapt instruction to individual learning needs.
• Bonus: Familiarity with graduate statistics curricula and common challenges, coupled with proficiency in supporting students across quantitative disciplines using statistical computing tools.