[Filled] Open Position: Research Scientist (Systematic Review of Critical Data Gaps)
Climate Change AI (CCAI) is seeking two researchers to conduct a systematic review of critical data gaps in the climate change and machine learning (ML) space. These researchers will review the literature and engage domain experts to assess the availability of existing datasets, assess where new or modified datasets could accelerate progress or remove bottlenecks at the intersection of climate change and ML, and publicly document the results of this research. Researchers may optionally pursue additional activities in line with these objectives (such as academic publications or web-based portals), to be scoped and defined with CCAI leadership. We thank DeepMind for providing CCAI with the funding for this position.
Geographic location is flexible within the United States, and day-to-day communication will be virtual, as the CCAI team is distributed worldwide. We are an interdisciplinary and international team and believe that diversity, inclusion, and equity are not only fundamental to the organization but also to progress in addressing climate change as a whole.
Start date: March 2023 or earlier (negotiable)
Location: Remote within the United States
Eligibility: The applicant must be legally entitled to work in the United States.
Compensation: $80K (USD)
Duration: 1 year
Deadline for application: Applications will be evaluated on a rolling basis.
Roles and responsibilities
- Review literature and engage domain experts to
- assess and taxonomize the existing datasets landscape for climate change and ML (availability, relevance, problems with existing datasets, etc.);
- assess and taxonomize datasets whose availability could accelerate progress or remove bottlenecks in different areas at the intersection of climate change and ML, and identify next steps for making them available.
- Identify and form an expert group of appropriate partners and stakeholders across relevant sectors, and carry out iterative consultations with this expert group.
- Update the CCAI Wiki and Dataset Wishlist, as well as generate any other appropriate publicly available documentation, based on the results of this work.
- With the consent of CCAI leadership, optionally pursue additional activities (such as academic publications or web-based portals) in line with the objectives of assessing and alleviating critical data gaps at the intersection of climate change and ML.
- Work closely in collaboration with the other researcher hired through this call, with supervision and potential collaboration from CCAI’s Executive Director and relevant CCAI core team volunteers, to monitor and execute on the above objectives.
Experience and requirements:
- A minimum of 3 years of full-time research experience relevant to climate change and/or machine learning. This can, but need not, include time spent towards a Ph.D. degree.
- Experience working on or with datasets (e.g., dataset preparation, data analysis and/or machine learning workflows, data evaluation metrics, data ethics).
- Experience working with stakeholders across multiple disciplines and/or in an interdisciplinary setting.
- Strong verbal and written communication skills, including in scientific settings
- Ability to work independently without close supervision.
- Full professional proficiency in spoken and written English.
- Demonstrated ability to plan and/or manage projects involving a medium-to-large number of stakeholders, across backgrounds and disciplines.
- Experience conducting large reviews and knowledge of relevant tools; experience with systematic review approaches is a plus.
- Experience conducting stakeholder interviews and/or consultations.
- Strong publication record in venues related to AI/machine learning, climate change, or related topics.
- Proficiency with data engineering workflows, data science programming packages, and/or modern machine learning libraries.
- Knowledge of languages in addition to English.
We look for the following in all CCAI team members, in addition to the role-specific requirements listed above:
- Mission alignment: Team members must be passionate about catalyzing impactful work at the intersection of climate change and machine learning, per our mission, goals, and guiding principles.
- Proactiveness and responsibility: Team members must be proactive and responsible to ensure our organization can run as effectively as possible.
- Attention to detail: Team members are mindful of the work that they carry out, which helps CCAI ensure high quality of its activities.
- Team ethic: Team members must be able to work productively and collaboratively with team members, and otherwise foster a collegial environment.
- Comfort working in a digital environment: CCAI’s team is based across the world, and communicates primarily using Slack and video conferencing tools. While we are happy to onboard newcomers to the particular technologies we use, team members must in general be comfortable working and collaborating in a primarily digital environment.
- Curiosity: CCAI is always embarking on new ventures to catalyze impactful work. We encourage new ideas from within (and outside) the team!
- Commitment to diversity, equity, and inclusion: We are an interdisciplinary and international team and believe that diversity, equity, and inclusion (DEI) are not only fundamental to the organization but also to progress in addressing climate change as a whole. Team members are expected to hold DEI as a central consideration when organizing and executing CCAI activities.
Climate Change AI (CCAI) is a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. Since it was founded in June 2019, CCAI has inspired, informed, and connected thousands of individuals from across academia, industry, and the public sectors, through its foundational reports on AI and climate change, networking and knowledge-sharing events, educational initiatives, and global grants programs. See the CCAI website for further details.
To apply, please send the following to email@example.com:
- Your current CV or resume
- Cover letter discussing why you are interested in this position and providing examples of relevant past experiences
- 1-2 samples of research, technical, or scientific writing (as attachments or links)
References may be requested at a later stage of the process.