Dear Sir or Madam, With great interest, I am writing in response to the advertisement for the Senior Data Scientist role. I have just completed two years working at the University of British Columbia in the Oceanography Department as a data scientist. My main research focused on creating, tuning, and deploying machine learning models to predict the appearance of phytoplankton blooms in the Salish Sea. I developed ML models that use only atmospheric drivers as input features and achieved very high predictive performance. I also developed a novel approach to identify extreme pollution events using meteorological and temporal parameters. In the past I worked for LuftBlick, a NASA/ESA spin-off, as an atmospheric scientist calibrating and analyzing air quality data. During my PhD at the University of Bremen, my research focused on Arctic amplification, specifically the satellite remote sensing of bromine monoxide (BrO) in the Arctic atmosphere. I derived the first published long-term dataset of tropospheric BrO for the Arctic using data from four satellites, and I implemented the first machine learning effort to model tropospheric BrO with an artificial neural network. I believe my data-science and ML experience, combined with my environmental background, would make me a strong fit for Equitable Earth as we pursue cutting-edge, data-driven sustainable solutions.…Dear Sir or Madam, With great interest, I am writing in response to the advertisement for the Senior Data Scientist role. I have just completed two years working at the University of British Columbia in the Oceanography Department as a data scientist. My main research focused on creating, tuning, and deploying machine learning models to predict the appearance of phytoplankton blooms in the Salish Sea. I developed ML models that use only atmospheric drivers as input features and achieved very high predictive performance. I also developed a novel approach to identify extreme pollution events using meteorological and temporal parameters. In the past I worked for LuftBlick, a NASA/ESA spin-off, as an atmospheric scientist calibrating and analyzing air quality data. During my PhD at the University of Bremen, my research focused on Arctic amplification, specifically the satellite remote sensing of bromine monoxide (BrO) in the Arctic atmosphere. I derived the first published long-term dataset of tropospheric BrO for the Arctic using data from four satellites, and I implemented the first machine learning effort to model tropospheric BrO with an artificial neural network. I believe my data-science and ML experience, combined with my environmental background, would make me a strong fit for Equitable Earth as we pursue cutting-edge, data-driven sustainable solutions.WWWW…