A day in the life of a clean tech data scientist

 We've all got pictures in our mind of what certain occupations entail - and this may be very different from what actually happens! When I started my career as an environmental engineer working in a consulting firm, I had this vision of working outdoors all the time, helping to save the planet, interacting with community members who were anxious to solve the problem...... but the actual work turned out to be quite different. I spent at least half of my time in the office filing out forms and paperwork that were needed to meet regulations, analyzing data and my interactions with community members usually meant chatting with them about the samples we were collecting, if we were going to be in their way, and the weather that day.

So what's a day in the life of a clean tech data scientist like?

Sadly, it's not just running around the office shouting "Eureka" because you found something amazing that will help the planet - though those days do come by! Most of the time it involves staring at computers and sometimes paper, trying to figure out which data are valuable and which contain errors and what exactly you need to solve the problem. 

The tasks a typical data scientist works on can be classified into 

  • Data capture through hardware (sensors, robots), apps or websites
  • Data storage and processing, including data cleaning
  • Modeling and analysis - which could include domain specific models (crop yield, watershed modeling for example), machine learning and statistics
  • Data visualization

Most data science work involves all four aspects; however, the time spent varies by the role and the type of problem being solved. For example, a role with a significant data engineering aspect to it could result in 80% or more of the time being spent on data storage and processing as opposed to modeling and analysis. On the other hand, a role with a focus on predictive modeling would likely already have engineering support for the data storage and processing components and majority of the time spent would be in model building, analysis and visualization of the results.

Clean tech data scientists typically spend more of their time doing modeling, prediction and visualization compared to the pure data engineering aspects of the role. That, however, does depend on the organization in question and the problem being solved. 

The question for the clean tech data scientist in any organization starts with - what is their primary role, what skills are needed to perform it and which tools or programs will be needed.

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