Jan Kazil

Background

About Jan Kazil

I am an accomplished applied scientist in atmospheric and climate research with a strong foundation in data analysis, scientific computing, and numerical simulation. My expertise includes modeling atmospheric chemistry and pollution using regional air quality models.I have over 5 years of experience using Python for data analysis and over 10 years of experience in scientific computing, including massively parallel computing.I use Python, TensorFlow, Keras, and Mask R-CNN. I have developed, trained, and operated neural networks for image object segmentation and for numerical model emulation. I have developed high-redundancy solutions for near-real-time, automated data extraction from the cloud (Amazon Web Services), with data analysis, visualization, and web deployment for mission planning.In addition to my technical skills, I have extensive experience leading and coordinating projects whose success depended on delivering a basis for critical decision-making from complex, uncertain data. As Forecast Team Lead and Flight Scientist during NOAA aircraft field campaigns, I developed forecasting methodologies, led teams in high-stakes environments, and implemented real-time data-driven decision-making. Furthermore I design and lead projects involving researchers across numerous international organizations, demonstrating my collaborative and organizational abilities.I thrive on learning and adapting to new challenges, consistently building upon my existing skills to deliver impactful results in evolving situations. I am highly motivated to leverage my skills and expertise to turn complex datasets into actionable insights, and to provide the basis for data-driven strategies and solutions.