Data Team

Data Scientist | Geospatial applications & remote sensing

Remote   |   Full Time

CREA (Centre for Research on Energy and Clean Air) is looking for a fully-remote Data Scientist, specialised in geospatial modeling and/or remote sensing to join us in our efforts to curb air pollution worldwide.

We are a new independent research organisation focused on revealing the trends, causes, and health impacts, as well as the solutions to air pollution. We use scientific data, research and evidence to support the efforts of governments, companies and campaigning organizations worldwide in their efforts to move towards clean energy and clean air.  We believe that effective research and communication are the key to successful policies, investment decisions and advocacy efforts.

As part of our data team, you will be leveraging the latest remote sensing and modeling technologies to build accurate and timely pictures of air pollution, tracking air pollution sources and emission intensities.

This is an exciting opportunity to work within a flexible environment and support both public health and climate change causes. The impact of this work can be tremendous, facilitating remote detection of falsified data that could cost thousands of lives.

This role reports to our Data Lead.

CREA is a fully remote organization, with current team members in Europe and Asia.

What you will do

As a data scientist & geospatial modeling / remote sensing specialist, your main responsibility would be to design, develop and put into production cutting edge models to identify important point source emissions of air pollutants (e.g. SO2 and NO2) and track their emission intensities in near real-time, using satellite-based sensors (e.g. TROPOMI), inversion modeling, meteorological reanalysis datasets and potentially ground-based measurements.

In the coming months, you would be focusing on:

  • reviewing the literature and identifying the most promising approach(es) (e.g. machine learning, box model first-principle). Some references are shared below;
  • implementing selected models using Python, R or any other language of your choice if justified by the task at hand;
  • devising validation procedures and assess models accuracy; and
  • if of interest, publishing methodology and results.

Depending on your skills and interests, another part of your work might entail supporting our other scientific computing and data projects.

Job Qualifications

We are looking for candidates who have:

  • demonstrable experience with advanced spatial analysis;
  • advanced command of R, Python (e.g. numpy, xarray), shell scripting, unix command line, and git;
  • strong knowledge of remote sensing principles as well as familiarity with meteorological and remote sensing datasets;
  • capacity to deploy models on cloud infrastructure; and
  • knowledge of air quality science is preferred, but not required.

About your character:

  • you are constantly eager to learn; and
  • you are detail-oriented and organized, and someone who takes pride in tidy and well-documented code.

What we can offer you

  • One-year contract - or - project-based (depending on your preference)
  • A remote & time-flexible working environment
  • Remuneration based on skills & experience, and competitive with standard pay
  • A meaningful and potentially very impactful mission
  • Health insurance
  • Membership to a co-working space

Useful references:

These are examples of models you would grow familiar with and might want to build upon (available upon request if beyond paywall):

  • Wang, S., Zhang, Q., Martin, R. V., Philip, S., Liu, F., Li, M., … He, K. (2015). Satellite measurements oversee China’s sulfur dioxide emission reductions from coal-fired power plants. Environmental Research Letters, 10(11), 114015. https://doi.org/10.1088/1748-9326/10/11/114015
  • Jiao, X., Liu, X., Gu, Y., Wu, X., Wang, S., & Zhou, Y. (2020). Satellite verification of ultra-low emission reduction effect of coal-fired power plants. Atmospheric Pollution Research, 11(7), 1179–1186. https://doi.org/10.1016/j.apr.2020.04.005
  • Lorente, A., Boersma, K. F., Eskes, H. J., Veefkind, J. P., van Geffen, J. H. G. M., de Zeeuw, M. B., … Krol, M. C. (2019). Quantification of nitrogen oxides emissions from build-up of pollution over Paris with TROPOMI. Scientific Reports, 9(1), 20033. https://doi.org/10.1038/s41598...

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