Statistics Graduate Level Tutor

Job Description

Statistics Graduate Level Tutor | Varsity Tutors

The Tone:
This is a contract role at Varsity Tutors, located remotely. Varsity Tutors builds an advanced technology platform, enhanced by AI and learning science, to create personalized learning experiences. This role matters by enabling tutors to connect with thousands of students nationally, making a real impact on their academic success and understanding in advanced statistics. By providing customized instruction, tutors help students achieve their specific learning goals from anywhere.

The TL;DR
• Role: Contract
• Type: Contract
• Location: Remote
• Pay: $40 hourly
• Mission: This person helps graduate-level students achieve academic success and understanding in advanced statistics through personalized, remote tutoring.
• Tech Stack: Live Learning Platform, AI-powered Tutor Copilot, R, Python

What You’ll Actually Do
• Teach: Provide personalized 1-on-1 online instruction to individual students on advanced statistical concepts and theories.
• Guide: Lead students through complex problem-solving, including likelihood function derivations, posterior distribution calculations, and experimental design.
• Prepare: Equip master’s and doctoral students for research methodology and advanced statistical analysis.
• Adapt: Adjust instruction using statistical computing software like R or Python, research paper examples, and proof-based exercises to support diverse student needs.
• Connect: Engage with students through scheduled personalized tutoring sessions and accept on-demand instant tutoring requests.

The Must-Haves
• Background: Graduate-level expertise in advanced statistics, including mathematical statistics, maximum likelihood estimation, hypothesis testing theory, and Bayesian inference.
• Experience: Proven ability to conceptually teach complex statistical derivations and guide students through designing experiments and implementing statistical models.
• Skills:
• Deep knowledge of asymptotic theory, Neyman-Pearson lemma, generalized linear models, regression analysis, multivariate methods, and nonparametric statistics.
• Proficiency in R or Python for statistical computing and model implementation.
• Strong communication and adaptive instruction skills, capable of explaining material using multiple approaches to meet individual learning needs.
• Familiarity with graduate statistics curricula and common challenges, such as understanding measure-theoretic probability foundations.
• Bonus: Capability to emphasize theoretical foundations and connect advanced statistics to research applications in biostatistics, econometrics, and machine learning.