I have a clean technology background - now what? Careers in Clean Technology and Data Science continued

Careers Clean Technology Data Science

We had a great time hosting our webinar on careers last month- a big thank you to all of you who signed up and attended and asked all those interesting questions! If you’re interested in getting the slides, they can now be downloaded for free here.

 

And now, to continue with our series of posts on this topic..

What kind of careers can be built at the intersection of clean technology and data science?

 

The figure above shows the sectors in both clean technology and data science. 

The kind of career you want to build will really depend on which end of the balance you’re sitting on. Are you someone who comes from a clean technology background and wants to pick up data science skills? Or are you someone who has a strong data science/software background and are interested in applying those skills to problems in clean technology? This will also govern the skills you will need to pick up or expand on as you build your career.

If you’re coming from a clean tech background, you’re probably someone who has a degree (Masters or PhD) in a clean technology field. Maybe you’ve done research on solar or wind energy or you’ve done projects as part of your Masters degree in water, energy and understand how to build a climate model or simulate a water system using existing software tools in the field. This means that when you are looking to work as a clean technology data scientist, you’ve already got strengths that you can play to.

First, you understand the problem that the company or organization is trying to solve. For example, let’s say that you’re someone who’s been working in agriculture or environmental problems associated with modern-day agricultural systems. That means that you understand how the system works - from weather governing crops, water systems and irrigation mechanisms providing water, to how soil-plant-water-chemical systems interact. If you’ve worked with farmers in the field, you also probably understand how farming equipment is used and have some understanding of how farmers think and what their needs are.

So, if you’re part of a company that is trying to improve crop yield or provide farmers with sensors or robots to improve their productivity - here’s what you already have to start working on your problem.

You know where to find the data and the models - weather data, crop yield data, historical water data, crop models etc. and that gives you a basis to start your work. If you’ve built simulations or models of the crop and farm system, then you know which parameters are essential and how to get the data to the point where it can be fed into a a model. If you’ve worked with R or Python, then you’re capable of figuring out how to build a statistical model or a machine learning model from existing packages. If you haven’t worked with these tools yet, then that’s something that you can learn as you go along. Or, you would be working with computer scientists and other data scientists in a team where you would be contributing your understanding of how the system works and they would be building the models.

Additionally, you’re probably someone who’s worked with GIS and is familiar with spatial data and data visualization. So while you wouldn’t be building the efficient, pretty looking dashboards and visuals to start with, you’re probably capable of generating prototypes maps that create spatial visualizations of the data.

As someone from a clean technology background, the part of doing data science that will require additional knowledge and skills would most probably be the data engineering aspects. This is the part where you have to figure out how to get the data into a system where it can be stored, accessed and retrieved reliably and efficiently. A software engineer is usually going to be much faster and more efficient at making this part of the system work since that’s essential coursework in computer science programs. However, what you would need would be the ability to build a prototype pipeline so that you can see that the data flows through and actually feeds into the model correctly and an ability to understand how to access data from the databases. Much of this work can be done using Python and SQL, but a working knowledge of algorithms and how they work is always useful.


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