Responsibilities
- Partner with research teams and educators to design data-driven research projects
- Advise on dataset design, metadata schema, sampling strategies, statistical methodologies and analytical and visualization tools
- Advice on dataset management, access, retrieval and storage options
- Design and maintain data ingestion, transformation, and quality-control pipelines for climate and environmental hazard datasets
- Collaborate with system engineers on databank architecture, data models, and metadata for research, education, and community users.
- Clean, manage, and analyze structured and unstructured datasets
- Apply statistical analysis, machine learning, and computational modeling techniques
- Develop custom analysis pipelines using programming and statistical tools
- Validate models and ensure methodological soundness
- Develop reproducible workflows using version control, documentation, and automation
- Support use of high-performance computing (HPC), cloud, or shared research infrastructure
- Promote best practices in data management, FAIR principles, and open science
- Assist with data sharing, archiving, and compliance with funding agency requirements
- Provide one-on-one consultations for researchers, teachers and students
- Develop and deliver workshops or short courses on data science methods and tools
- Create documentation, tutorials, and example code for common research workflows
- Produce clear data visualizations and summaries for academic and non-technical audiences
- Assist in preparing figures, tables, and supplementary materials for CAPTIVATE publications including the web portal
- Communicate complex analytical results clearly and effectively
Basic qualifications
- Master’s degree in data science, Statistics, Computer Science, or Environmental/Climate Science quantitative field Bachelor’s degree in data science, Statistics, or Computer Science with 1 year of applicable experience can be substituted
- Bachelor’s degree in data science, Statistics, or Computer Science with 1 year of applicable experience can be substituted
- Demonstrated experience supporting academic or scientific research
- Proficiency in Python or R and common data science libraries (e.g., pandas, NumPy, scikit-learn, or tidyverse).
- Experience with data pipelines (ETL/ELT), large/complex datasets, and SQL databases.
- Experience creating data visualizations and interactive tools (e.g., Dash, Shiny, JupyterNoteboooks or OpenOnDemand).
- Strong foundation in statistics and data analysis
- Experience with data visualization and reproducible research practices
- Excellent communication and collaboration skills
Preferred qualifications
- 3 years’ experience working in an academic or research-intensive environment
- Familiarity with machine learning, Bayesian methods, or Climate Science data sciences
- Experience with HPC, cloud computing, or scientific computational methods
- Experience in multi-institutional, grant-funded, or university/research settings.
- Teaching, mentoring, or workshop facilitation experience
- The above statements describe the general nature and level of work performed by individuals assigned to this job. It is not an exhaustive list of all duties and responsibilities required. Other duties may be assigned as determined by management.
- Reasonable accommodations may be made to enable individuals with disabilities to perform essential duties and responsibilities.
- Work Environment: Collaborative, interdisciplinary research support – team oriented Hybrid or remote work options may be available Opportunity to work on diverse, high-impact climate science data-oriented research projects
- Collaborative, interdisciplinary research support – team oriented
- Hybrid or remote work options may be available
- Opportunity to work on diverse, high-impact climate science data-oriented research projects
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