Statistics Graduate Level Tutor

Job Description

Statistics Graduate Level Tutor | Varsity Tutors

The Tone:
This is a freelance remote tutoring position with Varsity Tutors. The company operates a live learning platform, utilizing advanced technology, AI, and learning science to create personalized educational experiences. This role is crucial for supporting thousands of students nationally, helping them achieve academic success and deepen their understanding of graduate-level statistics.

The TL;DR
• Role: Freelance
• Location: Remote
• Pay: $40 hourly
• Mission: Support graduate-level students nationally by providing personalized instruction to help them achieve academic success and reach their learning goals.
• Tech Stack: Live Learning Platform, AI-powered Tutor Copilot, R, Python

What You’ll Actually Do
• Instruct: Provide personalized 1-on-1 online instruction to individual graduate-level statistics students.
• Support: Accept on-demand tutoring requests whenever available, offering instant support to students.
• Guide: Guide students through complex statistical concepts such as likelihood function derivations and ANOVA decompositions, connecting theoretical foundations to applications.
• Prepare: Prepare students for advanced research methodology and doctoral-level statistical analysis.
• Adapt: Adapt instruction using R or Python statistical computing, research paper examples, and proof-based exercises to support masters and doctoral students.

The Must-Haves
• Background: Deep knowledge of mathematical statistics, including maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian inference, regression analysis, multivariate methods, experimental design, and nonparametric statistics.
• Experience: Experience effectively teaching and breaking down complex graduate-level statistical concepts to students while also preparing them for advanced academic work.
• Skills:
1. Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models.
2. Proficiency in statistical computing using R or Python.
3. Strong adaptability to meet individual learning needs and styles.
4. Excellent communication skills and a friendly, engaging teaching style.