Intern, Threshold Modeling Support - Science-Based Targets For Land

Intern, Threshold Modeling Support - Science-based Targets for Land

Conservation International protects nature for the benefit of humanity. Through science, policy, fieldwork, and finance, we spotlight and secure the most important places in nature for the climate, biodiversity, and for people. With offices in 30 countries and projects in more than 100 countries, Conservation International partners with governments, companies, civil society, Indigenous peoples, and local communities to help people and nature thrive together.

POSITION SUMMARY - This position is based in Arlington-VA, Seattle-WA, or Santa Barbara-CA

This internship would run for a maximum of 4 months, for 35 hr/week, intended to begin in May 2024. Successful applicants are experienced in scientific modeling (statistical and GIS) and software. The intern would work primarily with Postdoctoral Associate Jordan Rogan and would be expected to assist in the completion of specific objectives of an established, ongoing research project, with the potential to become involved in other learning opportunities throughout the internship period pending completion of key tasks and outputs as outlined in objectives. Interns in this position would have the opportunity to contribute to cutting-edge science and research and contribute to a peer-reviewed publication based on research supporting the mission of the Science-based Targets Network, and specifically the forthcoming Science-based Targets for Land V2 guidance. The Science Based Targets Network (SBTN) aims to transform economic systems and protect the global commons - our air, water, land, biodiversity, and ocean. SBTN unites experts from leading environmental NGOs, including Conservation International, to collectively define what is necessary to do "enough" to stay within Earth's limits and meet society's needs.

RESPONSIBILITIES

• Prep, extract, and export spatial data points across an array of spatial data layers of interest (within Google Earth Engine) iteratively across global subregions for all relevant ecoregions at a global extent.

• Carry out modeling efforts in R programming language using imported spatial data variables in both machine learning models and segmented regression threshold models.

• Contribute to model selection and interpretation of results for ecological relevance and accuracy and relevance for SBTN purposes.

• Cross-evaluate model results for land indicators with relevant literature and research experts on a need-be basis and working group format to review and confirm ecological relevance and accuracy of results on a regional basis.

• Assist in the development of a decision tree approach and determine the most relevant and accurate thresholds from the existing scientific literature to use for particular variables if a threshold for a variable and ecoregion cannot be determined from a modeling approach.

• Compile derived and chosen thresholds for each variable and ecoregion into an Excel database for use by SBTN Land.

• Develop/expand on table cataloguing the most accurate and up-to-date thresholds from the current scientific literature to implement if a threshold cannot be determined for a variable in an ecoregion using modeling approach.

• Contribute to drafting of manuscript for publication on global extent ecoregion thresholds, time permitting.

Other duties as assigned by supervisor.

WORKING CONDITIONS

• Must be based within the vicinity of Seattle-WA, Arlington-VA, Santa Barbara-CA office. Regular weekly/biweekly virtual meetings will be held to check in on the progress of tasks with the supervisor.

• Guidance on flexible work arrangements will be shared during the interview process.

QUALIFICATIONS

Required

• Working towards a Bachelor's degree or a Master's degree/Doctoral program (not exceeding 3 months post-graduation) in the following fields of study environmental science, biological sciences, ecology, or a relevant field.

Preferred

• Experience and knowledge of R statistical programming language, Google Earth Engine and GIS software (e.g. ArcGIS), regression modeling and machine learning modeling experience (e.g. XGBoost or similar).

• Experience writing manuscripts for publication to peer-reviewed scientific journals.

To apply for this position please submit a resume and cover letter.

See all Conservation International Career Opportunities HERE

Conservation International is an equal opportunity, affirmative action, and Diversity, Equity, Inclusion, and Accessibility-committed employer. We are proud to have a diverse, global workforce where employment decisions are based on qualifications, experience, position requirements, business needs, market conditions, merit, and other legitimate nondiscriminatory factors.

As a science-based organization, CI follows CDC recommendations for COVID-19 and other vaccines. Accordingly, for the health and safety of our employees, their families, and our community, subject to applicable local law, all Conservation International staff are expected to be vaccinated against COVID-19. However, vaccination is no longer a mandate or condition of employment and employees are not required to provide proof of vaccination. This means we will not require an individual to be vaccinated to enter a CI office, attend CI events or travel on CI-related trips. This applies for our community partners as well. This vaccine expectation applies to all team members working remotely, in a hybrid work arrangement, and on-site. Job applicants are NOT required to state their COVID-19 vaccine status in their application.