Data Scientist I

Data Scientist I

A Bachelor's Degree in data science, statistics, bioinformatics, analytics, or similar field and two years of experience; Master's Degree in data science, statistics, bioinformatics, analytics, or similar field.

Statistical consulting with faculty, staff, and graduate students on applied research projects in agriculture, natural resources, and life sciences. Consulting activities include helping researchers define research questions, select appropriate statistical methods, evaluate experimental designs and sampling strategies, organize data for analysis, and interpret statistical results. Topics may include experimental design, ANOVA, regression, linear and generalized linear models, mixed models, repeated measures, multivariate analysis, and other applied statistical methods relevant to IFAS research.

Conducting reproducible statistical analyses using appropriate statistical software, primarily R and/or Python, and additional tools such as SAS, JMP, Stata, or SPSS as needed. Duties include data cleaning, exploratory data analysis, model fitting, model diagnostics, summarizing results, preparing analysis outputs, producing graphical representations of results, and explaining analytical approaches to researchers. The position may also use high-performance computing resources when appropriate for statistical or data-intensive analyses.

Assisting researchers with the preparation of publication-ready tables, figures, statistical summaries, methods descriptions, reports, abstracts, manuscripts, grant-related materials, and other research outputs. This includes helping ensure that statistical results are presented accurately, clearly, and in a format appropriate for scientific communication.

Supporting the development of statistical workflows, reproducible analysis scripts, and training materials for common consulting needs. This may include researching and evaluating R packages, Python tools, SAS procedures, Shiny applications, Google Colab notebooks, Jupyter notebooks, or other resources that may benefit IFAS researchers. These tools should support statistical analysis, teaching, consulting efficiency, or reproducible research rather than general software development.

Assisting with teaching, workshops, short courses, handouts, tutorials, and practical training activities related to applied statistics, experimental design, data analysis, data visualization, and reproducible workflows for faculty, staff, and students.

Contributing to the development and maintenance of online resources for the Statistical and Data Analytics Consulting Unit, including frequently asked statistical questions, example workflows, training materials, and guidance documents for common research design and data analysis issues.

$61,000 annually

A Bachelor's Degree in data science, statistics, bioinformatics, analytics, or similar field and two years of experience; Master's Degree in data science, statistics, bioinformatics, analytics, or similar field.

• Master’s degree in applied statistics, statistics, biostatistics, agricultural statistics, quantitative ecology, data science with strong applied statistical training, or a closely related quantitative field relevant to agriculture, natural resources, or life sciences.
• At least two years of experience applying statistical methods to real research data, preferably in an academic, research, consulting, or graduate statistical consulting environment. Experience with applied statistical analysis, experimental design, regression, ANOVA, linear models, generalized linear models, mixed models, and/or repeated-measures analysis.
• Proficiency in R and/or Python for statistical analysis and reproducible workflows.
  Ability to communicate statistical concepts, analytical results, and data limitations clearly to researchers from non-statistical backgrounds.
• Experience working with agricultural, biological, environmental, natural resources, plant science, animal science, ecological, or related applied research data is strongly preferred.
• This position is intended for candidates with strong applied statistical training and interest in research consulting. Applicants whose experience is primarily software engineering, computer science, database administration, or machine-learning model development without substantial applied statistical experience may not be the best fit for this role.
• Strong communication and interpersonal skills, with demonstrated ability to work effectively in collaborative, interdisciplinary academic or research environments.
• Demonstrated experience providing statistical support or consulting to researchers, including helping define research questions, selecting appropriate methods, explaining statistical assumptions, and interpreting results.
• Advanced experience with applied statistical methods such as linear mixed models, generalized linear mixed models, Bayesian analysis, experimental design, repeated measures, longitudinal data analysis, multivariate analysis, spatial analysis, or other methods relevant to agricultural and life-science research.
• Experience with statistical software such as R, SAS, JMP, Stata, SPSS, or Python, with strong preference for candidates who can develop clear, reproducible, and well-documented analysis scripts.
  Experience preparing statistical methods sections, publication-ready tables and figures, technical reports, manuscripts, or grant-related statistical summaries.
• Experience developing or delivering workshops, tutorials, training sessions, handouts, or open-source educational materials related to applied statistics, reproducible research, or data analysis.
• Experience with applied machine learning, artificial intelligence, deep learning, or data science methods is desirable when used to address agricultural, natural resources, biological, or life-science research questions. However, these methods are considered complementary to the core statistical consulting responsibilities of the position.

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.