Location: National Center for Ecological Analysis and Synthesis (NCEAS), University of California, Santa Barbara (UCSB)

Are you passionate about environmental science and data? Do you have a knack for teaching and supporting collaborative learning? We are seeking a collaborative, creative, and motivated Environmental Data Science Instructor to join the NCEAS Learning Hub team. This is an exciting opportunity to shape the future of environmental data science education and help environmental scientists gain the skills they need to navigate the evolving landscape of data science and open science practices.

As the Training Program Manager, you'll lead training events, develop and refine curricula, and facilitate collaborative learning for environmental scientists at various stages of their data science journey. Your contributions will be pivotal in developing innovative training programs that empower environmental professionals, researchers, and students to harness the power of data science in tackling complex ecological challenges.

What You'll Do:

• Lead & Facilitate Training Events: Organize and deliver training events in diverse formats such as hour-long workshops, multi-day in-person sessions, and virtual courses. Topics will range from R, Python, Git, data management, to open science and team science.
• Curriculum Development: Create and maintain comprehensive and accessible training materials tailored to various audiences, including graduate students, researchers, and industry professionals.
• Support Collaborative Research: Contribute to and support synthesis science research and collaborative learning in data science by facilitating projects such as the Gulf Ecosystem Initiative, Arctic Data Center, Morpho Initiative, and LTER Network Office.
• Enhance Environmental Data Science Programs: Play a key role in expanding our Learning Hub’s offerings, including introductory courses in environmental data science, and ensure training stays aligned with cutting-edge scientific trends.

What We’re Looking For:

• Bachelor's Degree (B.S., B.A.) in a relevant field.
• 1+ years experience in data science, including proficiency in R, Python, or other similar programming languages.
• Experience in teaching and facilitating data science training sessions or workshops.
• Familiarity with community management or involvement with collaborative, data-driven research groups.
• Experience with version control systems (e.g., Git and GitHub) and common communication tools like Slack and Zoom.
• An understanding of diversity, equity, and inclusion principles and a commitment to creating inclusive learning environments.
• An environmental science background or strong experience in engaging with environmental data science communities.

Preferred Qualifications:

• Master’s Degree in Environmental Science or related field.
• Proven ability to organize complex information and make it accessible for diverse audiences.
• Familiarity with data analysis, scientific research projects, and contributing to community-driven science initiatives.
• Experience utilizing open science and team science methods.

Why You Should Apply:

• Be part of an innovative and inclusive team at NCEAS, a leader in the field of environmental data science.
• Help shape the future of open science by empowering environmental scientists with the tools and skills they need.
• Collaborate with experts and thought leaders in environmental science and data science across the globe.
• Enjoy the scenic beauty and vibrant culture of Santa Barbara, California while working at UCSB, a top-tier research institution.

Application Requirements:

• CV/Resume – Please provide your most recent curriculum vitae.
• Cover Letter – A one-page cover letter that includes:
  • Your interest in the position.
  • Your qualifications for the role.
  • Your experience and/or commitment to supporting diversity, equity, and inclusion.
• Link to Example Code or Repository – Showcase your work by providing a link to a relevant coding example or project repository.
• References – Provide the contact information for 3 references (letters will only be requested from top candidates).