Lead Atmospheric Data Scientist

ClimaCell is revolutionizing weather forecasting by combining Weather-of-Things data -everything from cell tower transmissions to data from airplanes, drones and connected cars -with cutting edge models. The result: forecasts that are hyper accurate, specific, andcustomizable. We call it MicroWeather and we offer this street-by-street, minute-by-minuteaccuracy worldwide. Our customers are companies from weather-sensitive industries (Aviation,Construction, Energy, Outdoor Events etc), companies in the on-demand world, emergingeconomies, and people like you and me who simply don't want to get caught in the rain.

As an Atmospheric Data Scientist, you’ll lead our efforts to build new statistical forecasting systems which combine these observational and model to produce the best forecasts possible for our clients. You have a background in statistical applications in the geosciences, and understand how to extract the signal from the noise of many disparate forecasts. You’re comfortable wielding a diverse toolkit to tackle these problems, including ensemble/timeseries analysis techniques, bias correction procedures, and machine learning. A successful candidate will leverage their knowledge of these tools to prototype new statistical forecasts and analyses applied to massive meteorological datasets.

What You'll Be Doing
  • Lead initiatives to develop novel ensemble statistical analysis/post-processing systems to combine unique observations and model data to produce the best possible weather forecast
  • Develop novel applications for machine learning to build dynamic, self-correcting forecast systems which iteratively update and refine themselves as new data arrive into ClimaCell’s unique collection of weather observations
  • Help develop robust validation procedures  and conduct verification studies across the company’s data product portfolio, to ensure that our forecasts are always one step ahead of the changing weather

What You Bring
  • Extensive background in statistical and/or machine learning applications to weather forecasting and data analysis
  • Experience working with or developing  state-of-the-art ensemble forecasting systems and analyses, such as NOAA’s National Blend of Models or NCAR’s DiCast system
  • Knowledge of and familiarity with operational ensemble numerical weather prediction systems such as NOAA’s GEFS or ECMWF’s EPS
  • Experience building statistical modeling tools using scientific Python (particularly NumPy, pandas, scikit-learn, statsmodels, or related packages) or R 
  • Familiarity with Linux
  • 1-2+ years' industry experience, with formal or informal leadership experience 

Bonus points
  • Experience working on cloud computing systems, especially Amazon AWS or Google Cloud
  • Experience with other scientific Python libraries or frameworks, especially those used widely in the geosciences (SciPy, sklearn, skimage, xarray, Numba, etc.) or the R “tidyverse” (dplyr, purrr, broom, etc) 
  • Familiarity with building data processing pipelines and databases to support big data statistical analysis applications
  • A Masters or PhD in statistics, mathematics, meteorology, or any other field with corresponding coursework and application in atmospheric science

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