Remote Sensing Specialist (Associate Specialist), Environmental Markets Lab (emLab), University of California Santa Barbara

Position title: Remote Sensing Associate Specialist

Percent time: 100%

Anticipated start: Start date is negotiable, but ideally no later than April 1, 2022

Position duration: Expected appointment end date: March 31, 2023


Open date: December 17th, 2021

Next review date: Monday, Jan 17, 2022 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.

Final date: Sunday, Jul 31, 2022 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.


The Environmental Markets Lab (emLab) is an interdisciplinary team of scientists based at UC Santa Barbara that conducts cutting-edge, data-driven research on the power, limitations, and design of market-based approaches to tackle the world's most pressing environmental problems. In collaboration with implementing partners, emLab aims to better align environmental objectives and economic incentives in support of sustainable livelihoods and a resilient planet.

Increasingly, many emLab research projects make use of remotely sensed data to identify causal relationships between policy, human behavior, and environmental outcomes. To support this growing body of research, emLab is seeking a highly motivated Specialist to compile, process, and analyze remotely sensed data. Research projects will include, but not be limited to, (1) the effect of financial incentives on the adoption of agricultural and land use outcomes including conservation farming, alternatives to field burning, and reforestation, (2) the cost and equity impacts of policies seeking to conserve forests, and (3) the environmental impacts of development interventions. Projects span a diversity of geographies, including Africa, South America, and Southeast Asia.

The Remote Sensing Specialist will be responsible for a variety of tasks, including, but not limited to:
● Identifying appropriate remote sensing datasets and methods to produce accurate measurements of environmental conditions such as vegetation biomass, land use, and agricultural practices;
● Overseeing the collection and refining of training datasets;
● Developing and implementing machine learning models using remote sensing datasets;
● Developing efficient and reproducible workflows to implement best practices for data acquisition, pre-processing, and analysis;
● Collaborating with environmental economists to analyze remotely sensed outcomes in experimental and quasi-experimental settings.

The successful candidate will have strong quantitative skills, a creative and solutions-oriented mindset, and the ability to effectively communicate scientific results to stakeholders, scientists, and policymakers.

Position will remain open until filled.

The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching and service as appropriate to the position.



Basic qualifications (required at time of application)

Qualified applicants must have:
Bachelor’s degree in geography, environmental science and management, data science, statistics, natural resource economics, or a related field at the time of appointment.

Additional qualifications (required at time of start)

In addition, the candidate must have:
● At least 5 years of professional experience providing analytical research support to projects related to remote sensing, environmental economics, geography, or a related field;


● A Master’s degree in geography, environmental science and management, data science, statistics, natural resource economics, or a related field at the time of appointment.

Preferred qualifications

● Excellent quantitative skills including experience collecting, managing, processing, and analyzing large datasets;
● Experience working with remotely sensed data from a variety of sensors, including datasets with different spatial, spectral, and temporal properties, as well as data from both active and passive sensors;
● Proficiency with coding languages such as R, Python, the Google Earth Engine API, and/or Stata;
● Familiarity with the application of machine learning methods to remotely sensed data;
● Experience or proven interest in the use of econometric methods of causal inference;
● Ability to synthesize and communicate scientific methods and results to a variety of partners and stakeholders;
● Excellent creative problem solving and troubleshooting skills;
● Excellent verbal and written communication skills;
● Ability to take initiative, self manage, and work independently as well as collaboratively as part of a multidisciplinary team;
● Exceptional attention to detail and strong organizational and time management skills.


Document requirements

  • Curriculum Vitae - Your most recently updated C.V.
  • Cover Letter

Reference requirements

  • 3 required (contact information only)

The search committee will contact references of the top candidate(s) after the interviews.
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Help contact:


The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.

As a condition of employment, you will be required to comply with the University of California SARS-CoV-2 (COVID-19) Vaccination Program Policy All Covered Individuals under the policy must provide proof of Full Vaccination or, if applicable, submit a request for Exception (based on Medical Exemption, Disability, and/or Religious Objection) or Deferral (based on pregnancy) no later than the applicable deadline. New University of California employees must (a) provide proof of receiving at least one dose of a COVID-19 Vaccine no later than 14 calendar days after their first date of employment and provide proof of Full Vaccination no later than eight weeks after their first date of employment; or (b) if applicable, submit a request for Exception or Deferral no later than 14 calendar days after their first date of employment. (Capitalized terms in this paragraph are defined in the policy.) Federal, state, or local public health directives may impose additional requirements.


Santa Barbara, CA