The FOR-NM Project (Forest Research for New Mexico Water and Carbon Management) seeks applications for a Research Scientist 3 position with a focus on data management.

We are looking for a motivated and organized person with a background (MS degree preferred) in ecology, data analytics, remote sensing, GIS, and/or hydrology to join our team. This multi-institutional research program spans five institutions across the state of New Mexico including collaboration with Sandia National Laboratory. The project aims to improve management of New Mexico’s forested watersheds by addressing key gaps in understanding of water and carbon dynamics in relation to forest management.

Duties include collating and managing data with version-controlled programmatic workflows in R and/or Python, including large volumes of high-frequency sensor data and model outputs; working with investigators to design and deliver data products to open access repositories; and training participants to track and quality control data. Interest in participating in field work as approximately 15% of duties is strongly desired. Expertise in working with environmental sensor data and geospatial data (including remote sensing products) is required.

This position will be based at the University of New Mexico in Albuquerque, NM. Partial but not fully remote work options can be considered for exceptional candidates.

The position will start as soon as possible and will be hired on a 1-year contract, with the expectation of renewal for additional years pending satisfactory performance. The position’s online application is open and applications will be reviewed on a rolling basis.

See the Position Description for additional information.

• Successful candidate must submit to a post-offer, pre-employment physical examination and medical history check. Additional health screenings may be required, dependent on the nature of the research conducted, as determined by Employee Occupational Health Services.

Bachelor's degree; at least 3 years of experience directly related to the duties and responsibilities specified.

Completed degree(s) from an accredited institution that are above the minimum education requirement may be substituted for experience on a year for year basis.

Preferred Qualifications:

• Masters in ecology, data analytics, remote sensing, GIS, hydrology, or closely related field
• Previous experience working with large datasets
• Experience working collaboratively with a team of scientists across multiple institutions
• Experience with numerical modeling, remote sensing, and/or environmental sensors
• Excellent English verbal communication skills
• Experience with programming in R and/or Python, and collaborative version-controlled workflows including Git and/or Jupyter Notebooks
• Experience with developing data processing pipelines using R and/or Python

Only applications submitted through the official UNMJobs site will be accepted. If you are viewing this job advertisement on a 3rd party site, please visit UNMJobs to submit an application.

Positions posted with a Staff Type of Regular or Term are eligible for the Veteran Preference Program. See the Veteran Preference Program webpage for additional details.

The University of New Mexico is committed to hiring and retaining a diverse workforce. We are an Equal Opportunity Employer, making decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability, or any other protected class.

The University of New Mexico requires all regular staff positions successfully pass a pre-employment background check. This may include, but is not limited to, a criminal history background check, New Mexico Department of Health fingerprint screening, New Mexico Children, Youth, and Families Department fingerprint screening, verification of education credentials, and/or verification of prior employment. For more information about background checks, visit https://policy.unm.edu/university-policies/3000/3280.html. Refer to https://policy.unm.edu/university-policies/3000/3200.html for a definition of Regular Staff.