Data Analyst
Vanderbilt University
IT, Data Science
Nashville, TN, USA
About the Logan Lab
The Logan Lab, directed by Dr. Jessica Logan at Vanderbilt University's Peabody College, studies the research methods that scientists use to understand how children learn over time, with a particular focus on the academic skills of children with or at risk for learning disabilities. The lab is also committed to advancing data management, data sharing, and data quality practices across education and developmental science research.
Our work is supported by federal funding, including grants from the National Institutes of Health, and spans multiple active projects. This is an opportunity to join a collaborative, intellectually rigorous team and contribute meaningfully to research that has real-world implications for children's learning and development.
Position Overview
The Data Analyst will play a central role in supporting the empirical work of the Logan Lab across multiple federally funded research projects. This position is well-suited for a quantitatively skilled researcher who thrives on working with complex datasets, values methodological rigor, and wants to contribute to publishable scientific work. The analyst will work closely with Dr. Logan and other lab members on all phases of the research process, from data preparation to dissemination.
Key Responsibilities
Data Management & Quality
Clean, organize, and document datasets from multiple concurrent research projects in accordance with best practices for reproducibility and data sharing
Build and maintain data management systems, codebooks, and standard operating procedures
Conduct quality assurance checks to ensure data integrity across collection, entry, and storage pipelines
Support compliance with federal data sharing and open science requirements (e.g., NIH Data Management and Sharing Policy)
Data Analysis
Conduct quantitative analyses including descriptive statistics, regression modeling, longitudinal and growth curve models, and other methods appropriate to grant aims
Work with large, complex datasets from educational and developmental research contexts
Select and apply appropriate statistical methods to address research questions related to child development and academic outcomes
Develop and maintain well-documented, reproducible analysis scripts (e.g., in R or Python)
Collaborate with the PI and lab members to interpret findings and plan follow-up analyses
Scholarly Writing & Dissemination
Contribute to the writing and revision of peer-reviewed manuscripts for publication in developmental and educational science journals
Prepare data summaries, tables, and figures suitable for manuscripts, grant reports, and presentations
Support the preparation of progress reports and other deliverables required by federal funding agencies
Assist with literature reviews and background sections for manuscripts and grant materials
Collaboration & Lab Contribution
Work collaboratively with postdoctoral researchers, doctoral students, and master's students on shared projects
Participate in lab meetings, journal clubs, and research discussions
Assist with grant-related tasks as needed, including supporting new proposals
Contribute to a positive, inclusive, and productive lab environment
Qualifications
Required
Bachelor's degree in quantitative methods, statistics, psychology, education, data science, or a closely related field
Strong quantitative skills and demonstrated experience with statistical analysis
Proficiency in at least one statistical computing environment (R strongly preferred; Python, Stata, or SAS also considered)
Experience with data cleaning and management of real-world, messy datasets
Excellent written communication skills, including the ability to write clearly for scientific audiences
Ability to work independently, manage multiple project timelines, and meet deadlines
Strong attention to detail and commitment to reproducible, transparent research practices
Preferred
Master's degree in quantitative methods, educational measurement, biostatistics, developmental psychology, or a related quantitative field
Experience with longitudinal data analysis, multilevel modeling, or structural equation modeling
Background in or knowledge of child development, learning disabilities, or education research
Experience contributing to peer-reviewed publications or conference presentations
Additional Information
This is a full-time, exempt position (40 hours per week)
Position is grant-funded; continued employment is contingent on funding availability
Salary is commensurate with education and experience
Vanderbilt University is an equal opportunity employer committed to building a diverse and inclusive community
At Vanderbilt University , our work - regardless of title or role - is in service to an important and noble mission in which every member of our community serves in advancing knowledge and transforming lives on a daily basis. Located in Nashville, Tennessee, on a 330+ acre campus and arboretum dating back to 1873, Vanderbilt is proud to have been named as one of “America’s Best Large Employers” as well as a top employer in Tennessee and the Nashville metropolitan area by Forbes for several years running. We welcome those who are interested in learning and growing professionally with an employer that strives to create, foster and sustain opportunities as an employer of choice.
We understand you have a choice when choosing where to work and pursue a career. We understand you are unique and have a story. We want to hear it. We encourage you to apply today so that you might become a part of our story.
Position Overview The Data Analyst will play a central role in supporting the empirical work of the Logan Lab across multiple federally funded research projects. This position is well-suited for a quantitatively skilled researcher who thrives on working with complex datasets, values methodological rigor, and wants to contribute to publishable scientific work. The analyst will work closely with Dr. Logan and other lab members on all phases of the research process, from data preparation to dissemination.