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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><a href="https://www.climacell.co/careers/">https://www.climacell.co/careers/</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal style='mso-margin-bottom-alt:auto;background:#F6F6F6'><span style='font-size:12.0pt;font-family:Roboto;color:black'>Lead Atmospheric Data Scientist<o:p></o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><span style='font-family:Roboto;color:black'>Full time<o:p></o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><span style='font-family:Roboto;color:black'>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.<o:p></o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><span style='font-family:Roboto;color:black'><o:p> </o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><span style='font-family:Roboto;color:black'>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.<o:p></o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><b><span style='font-family:Roboto;color:black;letter-spacing:.3pt'><br><strong><span style='font-family:Roboto'>What You'll Be Doing</span></strong></span></b><span style='font-family:Roboto;color:black'><o:p></o:p></span></p><ul type=disc><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l0 level1 lfo1;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>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<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l0 level1 lfo1;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>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<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l0 level1 lfo1;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>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<o:p></o:p></span></li></ul><p class=MsoNormal style='background:#F6F6F6'><span style='font-family:Roboto;color:black'><o:p> </o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><strong><span style='font-family:Roboto;color:black;letter-spacing:.3pt'>What You Bring</span></strong><span style='font-family:Roboto;color:black'><o:p></o:p></span></p><ul type=disc><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo2;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>Extensive background in statistical and/or machine learning applications to weather forecasting and data analysis<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo2;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>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<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo2;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>Knowledge of and familiarity with operational ensemble numerical weather prediction systems such as NOAA’s GEFS or ECMWF’s EPS<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo2;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>Experience building statistical modeling tools using scientific Python (particularly NumPy, pandas, scikit-learn, stats models, or related packages) or R <o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo2;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>Familiarity with Linux<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l2 level1 lfo2;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>1-2+ years' industry experience, with formal or informal leadership experience <o:p></o:p></span></li></ul><p class=MsoNormal style='background:#F6F6F6'><span style='font-family:Roboto;color:black'><o:p> </o:p></span></p><p class=MsoNormal style='background:#F6F6F6'><strong><span style='font-family:Roboto;color:black;letter-spacing:.3pt'>Bonus points</span></strong><span style='font-family:Roboto;color:black'><o:p></o:p></span></p><ul type=disc><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>Experience working on cloud computing systems, especially Amazon AWS or Google Cloud<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>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) <o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>Familiarity with building data processing pipelines and databases to support big data statistical analysis applications<o:p></o:p></span></li><li class=MsoNormal style='color:black;mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3;background:#F6F6F6;box-sizing: border-box'><span style='font-family:Roboto'>A Masters or PhD in statistics, mathematics, meteorology, or any other field with corresponding coursework and application in atmospheric science<o:p></o:p></span></li></ul><p class=MsoNormal><o:p> </o:p></p></div></body></html>