Data Management Analyst II

Classification Title

Data Management Analyst II

Master’s degree in an appropriate area; or a bachelor’s degree in an appropriate area and two years of relevant experience.

Consulting with students, staff, and faculty on:

• Data analysis questions, both statistical and mathematical
• Best practices for data representation
• Data storage, formatting, and querying
• Computer programming
• Design of experiments and sampling techniques
• Artificial intelligence methods
• Developing experimental protocol, including survey instruments

Interpreting and running data analysis, using high‑performance computers, formatting output, explaining analysis approaches, and interpreting analysis results. Ensure compliance with data security standards.

Preparing statistical and narrative reports and recommendations. Supporting research staff, students, and faculty with the creation of publication‑ready tables and graphs, and other work associated with project reports to publication.

Researching new software solutions, such as R‑packages, Python, Google Colab, Jupyter Notebook, Shiny App, SAS PROCs or macros, that might be useful to Statistical and Data Analytics Consulting Unit clients. Collaborating with IT staff on improvements to or development of data infrastructure.

Responsible for teaching and developing handouts for practical data analysis in applied statistics courses, short courses, and workshops. May develop and/or present training programs.

Developing and maintaining a website to answer common statistical questions that have arisen in consulting sessions.

Monday through Friday, 8 AM to 5 PM.

$61,000‑63,000 commensurate with education and experience.

Master’s degree in an appropriate area; or a bachelor’s degree in an appropriate area and two years of relevant experience.

Master’s degree in applied statistics or an equivalent field, with at least two years of experience as a graduate statistical consulting assistant. Experience with artificial intelligence, including machine learning and deep learning methods, applied to agriculture, natural resources, and life sciences applications. Proficiency in programming using either R or Python, along with a good working knowledge of at least one additional statistical software package such as SAS, JMP, Stata, or SPSS.

Strong communication and interpersonal skills, with demonstrated ability to work effectively in collaborative, interdisciplinary academic environments. Proven capacity to clearly present, interpret, and visualize research findings for academic, professional, and public audiences.

Advanced proficiency in statistical analysis, including Linear Mixed Models (LMM), Generalized Linear Mixed Models (GLMM), Bayesian analysis, experimental design and analysis, and comprehensive data cleaning and preprocessing workflows.

Demonstrated experience in developing and delivering statistical workshops and training sessions. Experience producing open‑source materials and tools, including R packages, Shiny applications, and interactive resources developed using platforms such as Google Colab and Jupyter Notebook.

In order to be considered, you must upload your cover letter and resume.

Application must be submitted by 11:55 p.m. (ET) of the posting end date.