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 aims to transform the way people learn by leveraging advanced technology, AI, and learning science to create personalized learning experiences. This role is crucial for supporting thousands of students nationally by providing customized instruction and fostering academic success and understanding in graduate-level statistics. You will make a significant impact on students’ learning journeys from the comfort of your home.

The TL;DR
• Role: Contract
• Location: Remote
• Pay: $40 hourly
• Mission: Provide personalized instruction to graduate-level students in statistics, enhancing their academic success and understanding.
• Tech Stack: Live Learning Platform, AI-powered Tutor Copilot, R or Python statistical computing

What You’ll Actually Do
• Teach Advanced Statistics: Deliver deep knowledge of mathematical statistics, including maximum likelihood estimation, Bayesian inference, and regression analysis.
• Explain Complex Concepts: Break down intricate topics such as likelihood function derivations, posterior distribution calculations, and ANOVA decompositions.
• Guide Problem-Solving: Direct students through designing experiments, performing multivariate analysis, and implementing statistical models in software.
• Adapt Instruction: Adjust teaching methods using R or Python statistical computing, research paper examples, and proof-based exercises for master’s and doctoral students.
• Address Curriculum Challenges: Identify and explain concepts students commonly struggle with, such as measure-theoretic probability foundations and interpreting complex model outputs.

The Must-Haves
• Background: Graduate-level proficiency in advanced statistics and mathematical concepts.
• Experience: Experience preparing students for research methodology and doctoral-level statistical analysis. Practical experience guiding students in designing experiments, performing multivariate analysis, and implementing statistical models in software.
• Skills: Deep knowledge of mathematical statistics; ability to explain complex concepts such as likelihood function derivations and posterior distribution calculations; strong communication and engaging teaching style; adept at adapting instruction to individual learning needs.