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 transforms the learning experience through the innovative application of advanced technology, AI, and cutting-edge learning science to create highly personalized learning experiences. Tutors on the platform play a crucial role in significantly impacting students’ academic success and understanding, fostering improved outcomes and nurturing a lifelong passion for learning. The company’s mission is to empower students to achieve their academic goals through customized 1-on-1 instruction.

The TL;DR
• Role: Freelance
• Type: Contract
• Location: Remote
• Pay: $40 hourly
• Mission: Deliver highly personalized, in-depth instruction to individual students to achieve their academic goals and understanding of graduate-level statistics.
• Tech Stack: Live Learning Platform, AI-powered Tutor Copilot, R, Python

What You’ll Actually Do
• Deliver personalized, in-depth instruction to individual students tailored to their specific needs and learning styles.
• Prepare masters and doctoral students for research methodology and advanced statistical analysis.
• Guide students through designing experiments, performing multivariate analysis, and implementing statistical models in software.
• Explain complex statistical concepts, emphasizing theoretical foundations and connecting them to real-world research applications.
• Adapt instruction effectively using statistical computing, relevant research paper examples, and proof-based exercises.

The Must-Haves
• Background: Graduate-level understanding of advanced mathematical statistics, with the capability to prepare students for doctoral-level analysis.
• Experience: Demonstrated skill in breaking down intricate statistical concepts, guiding students through problem-solving, and familiarity with common graduate statistics curricula and student challenges.
• Skills: Deep knowledge of mathematical statistics including maximum likelihood estimation, Bayesian inference, regression analysis, multivariate methods, experimental design, and nonparametric statistics; Ability to explain complex concepts such as asymptotic theory, the Neyman-Pearson lemma, and generalized linear models; Proficiency in R or Python for statistical computing; Strong communication skills and a friendly, engaging teaching style.