Detecting Gender-Bias in Job Market for Energy Modelers / Engineers using nltk and Python

Yung Codes
2 min readMar 12, 2020

In the spirits of #internationalwomenday2020 and #womenhistorymonth, I used data science to detect gender-bias in the job market, specifically to my career field as an energy modeler at the time. In doing so, I also found out about how the market bias reflects in my own resume.

This was one of my first personal coding project.

Background

In 2018, I started to learned more about data, web-scraping, and the addictive instant gratification of making data move.

While I was still an energy modeler, I work with data every day, but something about looking at non-deterministic approach was different.

My inner cat was curious.

So I made a personal project to look at the job landscape of energy modelling careers, since it was a niche and specialized field. What are employers looking for? Is there a future for energy modelers? I submitted to SimBuild 2018, but in all honestly, I didn’t go as far as I had hoped.

By summer 2019, I’ve sharpened my home-brew skills and practice I have read about natural language processing (NLP) and sentimental analysis. This time, I wanted to answer a big-girl question and dig a little deeper.

I wanted to know:

1. What can the webscrape data tell us about the energy modeling job market?

2. Is there gender-bias in the energy modeling job market?

Some ~48 hours later …

..collectively over a few months.

Here is what the data revealed:

Github Repo.

Were you surprise?

Elizabeth weren’t.

My initial hypothesis was that there are likely closer to neutrality because I’ve met many fellow women engineers in energy modeling events more so than in HVAC design. It’s still a man’s world in many STEM career fields, especially in HVAC/construction and even including data science. Despite the odds, the women will keep going. On top of child-bearing, hormones cycles, and dealing with our feelings with our thinking hat on.

Are there gender-bias in your job market? Check out this gender decoder calculator to attain a quick insight. I’d love to hear your story.

--

--

Yung Codes

What I learn about #energymodeling #climatechange #programming #datascience. Update at least once a month.