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 operates a Live Learning Platform, leveraging advanced technology and AI to create personalized learning experiences. This role is crucial for significantly impacting students’ academic success and deep understanding of complex statistical concepts through customized, one-on-one instruction. Tutors empower masters and doctoral students to achieve their learning goals in quantitative disciplines.

The TL;DR
• Role: Contract
• Location: Remote
• Pay: $40 hourly
• Mission: Guide graduate students in mastering advanced statistical concepts and applications to significantly enhance their academic success.
• Tech Stack: R, Python statistical computing

What You’ll Actually Do
• Tutoring: Provide personalized 1-on-1 online instruction to individual students across various advanced statistics topics.
• Conceptual Guidance: Break down complex statistical concepts, such as likelihood function derivations, posterior distribution calculations, and ANOVA decompositions, for student comprehension.
• Application Support: Guide students in designing experiments, performing multivariate analysis, constructing confidence regions, and implementing statistical models in software.
• Curriculum Alignment: Adapt instruction using R or Python, research paper examples, and proof-based exercises to support masters and doctoral students.
• Problem Solving: Identify common student struggles and explain material using multiple approaches, adapting to individual learning needs and styles.

The Must-Haves
• Background: Graduate-level proficiency in advanced mathematical statistics, including maximum likelihood estimation, Bayesian inference, hypothesis testing theory, and generalized linear models.
• Experience: Experience preparing students for research methodology and doctoral-level statistical analysis, emphasizing both theoretical foundations and practical applications.
• Skills:
1. Deep knowledge of mathematical statistics, including asymptotic theory and the Neyman-Pearson lemma.
2. Ability to explain complex derivations and guide through experimental design and multivariate methods.
3. Proficiency in R or Python for statistical computing and model implementation.
4. Strong communication skills with a friendly, engaging, and adaptable teaching style.