[ES_JOBS_NET] Lead Atmospheric Data Scientist, ClimaCell, Boston, MA

[Christine Wiedinmyer]

https://www.climacell.co/careers/

 

Lead Atmospheric Data Scientist

Full time

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, and customizable. We
call it MicroWeather and we offer this street-by-street, minute-by-minute
accuracy worldwide. Our customers are companies from weather-sensitive
industries (Aviation, Construction, Energy, Outdoor Events etc), companies
in the on-demand world, emerging economies, 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/time series 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, stats models, 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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