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.

What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
Read more

Our online community space is now live!

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !
Read more

How much water should an email consume? Data centers and water use

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
Read more