Biodiversity AI/ML Ecosystem Data Analyst

The University of Vermont; Burlington, Northern (VT, KY); Hybrid; USD 80,000 - 95,000; Full time

Benefits offered by this job

Hybrid work arrangement

Job summary

The University of Vermont is seeking a Research Data Analysis PC5 X to advance AI/ML workflows in ecological data analysis. You will design, train, evaluate, and deploy AI/ML models for mapping, classification, and detection of wildlife and other features, building pipelines and interfaces for reproducible research.

In collaboration with the Spatial Analysis Lab and RSENR, you will support AMBER and other programs, contribute to grant writing, and pursue outside funding opportunities.

Qualifications

• PhD in a relevant field and four years of related professional experience.
• Expertise in data science, machine learning, and AI applications.
• Strong proficiency in relational databases and SQL-based systems (e.g., SQLite, Postgres).
• Experience with cloud-native infrastructure and programming in R and Python.

Responsibilities

• Analyze ecological data and design, build, and operationalize AI/ML workflows for data processing and insights.
• Develop data pipelines, databases, and interfaces (R Shiny or similar) to enable reproducibility and accessibility.
• Collaborate with SAL, RSENR, and partners on grant writing, research initiatives, and outreach.
• Lead or contribute to grant writing and development of scalable AI workflows.

Skills

PhD in relevant field

Data science

Machine learning

Relational databases

SQL/Postgres/SQLite

APIs/web services

Cloud-native infra

R

Python

Public/agency partnerships

Education

PhD in Computer Science, Wildlife Biology, Ecology, Data Science, Bioinformatics, or closely related

Tools

SQLite

Postgres

SQL

Power Automate

Teams

SharePoint

R

Python

Job description

The University of Vermont is seeking a Research Data Analysis PC5 X to advance AI/ML workflows in ecological data analysis. You will design, train, evaluate, and deploy AI/ML models for mapping, classification, and detection of wildlife and other features, building pipelines and interfaces for reproducible research.

In collaboration with the Spatial Analysis Lab and RSENR, you will support AMBER and other programs, contribute to grant writing, and pursue outside funding opportunities.