‘Why Weather Analytics’ is a monthly series about all of the hackers, entrepreneurs, teachers, tornado-chasers and weatherheads who make up the body of Weather Analytics, how they got here, and why. Each month we’ll focus on a new employee, their story, and what about Weather Analytics pulled them in.
This month we’re featuring Dr. Ellen Cousins, Data Scientist at Weather Analytics, and how her work at Dartmouth College and NCAR has informed her work at Weather Analytics.
Dr. Cousins is a data scientist at Weather Analytics, where she uses machine learning and large-scale computing to turn weather data into actionable information. Through deep statistical analysis and the fusion of weather with external data sets, she has developed real-time predictive models of crop production, has identified relationships between weather conditions and sales for a prospect in the retail sector, and has also developed tools to estimate location-specific risk of high wind gusts. Additionally, she has contributed to creating and updating a large-scale web-enabled database of weather data running in the cloud.
1) Tell me about your career path that led you to this role as a Weather Analytics data scientist? What was the focus of your past research and work at NCAR and Dartmouth?
Ever since a research internship I did as an undergrad, I have been interested in the role of data analysis/statistics in solving challenging technical & scientific problems. This interest carried over into my PhD work at Dartmouth. I studied space physics and electrical engineering, but my research was based on very large data sets of space physics-related observations. I focused particularly on a decades-long collection of observations from an international network of radars that measure properties of the Earth’s ionosphere (the electrically charged component of the upper atmosphere). I worked on tools to transform the raw data into information that other scientists could easily incorporate into their own research, and I applied new statistical/computing techniques to look for patterns in the observations that I leveraged to develop predictive models.
My postdoctoral work at NCAR was a continuation of my PhD work. I continued to add to my statistics/computing toolbox, and I applied these tools to develop data products and predictive models using several large Earth & space-based data sets. One problem I worked on involved filling in gaps in information about the ionosphere using whatever data was available, together with statistical modeling. The resulting gap-free and stable output is much easier to ingest into other studies or models than the raw observations.
2) How did you first hear about Weather Analytics?
I was nearing the end of my postdoctoral fellowship and exploring options for my next career step. A Weather Analytics Data Scientist job posting showed up in my LinkedIn feed and caught my attention.
3) What inspired your move to Weather Analytics?
When finishing up my postdoc, I knew that I wanted to try out a position in industry as opposed to academia, primarily because I wanted to see the impacts of my work more quickly and I wanted the opportunity to apply my stats/computing experience to solving a variety of real-world problems.The Weather Analytics Data Scientist position was a perfect step for me. There were enough similarities to my previous work (geospacial data and a connection to geosciences) that I could hit the ground running, but enough differences to make it new and challenging. I liked that the small size and fast pace of a start-up would allow me to get experience with many facets of a data scientist role, rather than be siloed into one specific focus area, and the wide variety of projects to work on would always keep things interesting.
4) Tell me about your job now and what are its core components.
I’m involved in the data side of Weather Analytics at all stages of the data. I work on the tools used to transform the original weather data into something that’s easily accessible and usable. I’m involved in work to create new products from the weather data. And I work on fusing weather data with non-weather data to find correlations and build predictive models. Two aspects of the work have been particularly interesting. The first is finding ways to deal with data at a much larger scale than I’d done before (I’ve learned a lot about databases and I’ve gotten better at writing memory & compute efficient code). The second is in the area of data fusion: it was rewarding to apply basic principles to build from the ground up a predictive model in the area of crop production, something I had no prior experience in.
5) You haven’t yet worked for Weather Analytics a full year yet, but what has been your experience so far working for this company in particular? How have you liked it? What do you like most about your job?
I have loved my job here so far. I enjoy getting to work with a group of really smart people from a variety of different backgrounds. I like the balance of applying existing skills and learning new ones, and the balance of independent and teamwork. And I like seeing my work put into use so quickly and being used by customers for real-world applications.