Forging a path to tech through economics — Data Scientist Susan Chang on how curiosity propelled her machine learning career
Economics and machine learning might not seem like they have a lot in common, but Susan Chang found a common thread.
“Economics uses data and statistics to generate insights, which is the foundation of machine learning, too,” she says. “Machine learning is very broad. You’re using data and algorithms to analyze and predict things that are happening in the world.”
Susan, a Principal Data Scientist on Elastic®’s Security Machine Learning team, has worked in machine learning for a few years now but didn’t have the most straightforward path to get here.
She graduated with a master’s degree in economics and, during her studies, taught herself to program with the help of one university course and a lot of self-study.
“Because of the way the [economics] curriculum was taught, it wouldn’t have been enough to enter machine learning,” she says. “I needed my own programming skills.”
Her original interest in programming started with a love of video games, and she went on to found a small studio that designs and builds games and is still operating, albeit on a smaller scale these days.
“It started as just me,” Susan says. “I would write the story and hire artists and I programmed the game myself. Being experienced in programming helps in my day job. They have similar skill sets.”
At Elastic, Susan uses machine learning to detect threats.
“Right now I’m working on a research project to help customers detect malicious attacks using generative AI,” she says. “My team makes the most of current technology and new technology and combines it to help our customers protect their data.”
Susan has always enjoyed helping people, and with Elastic’s distributed workforce and flexible working culture, she’s able to continue her personal ventures, too. She started mentoring others looking to get into machine learning, and it’s grown exponentially.
“A few years ago I opened up free calendar slots to help people ask questions about machine learning,” she says. “Even before that, I got messages on LinkedIn and when I would speak at conferences, people asked me questions about how to break into or succeed in the field.”
Now, Susan is working on a book about how to get into the machine learning field.
“It was hard to help everyone, so I tried to scale through conferences and a book,” she says. “I can reach more people and help them in their career and enter the field.”
Susan was inspired to enter the tech industry after seeing other visible women in tech and informal mentoring with friends.
“It encouraged me,” she says. “By mentoring and being visible, it’s a form of inspiration. I spend time being visible because if someone won’t reach out, I can still have that effect.”
Her biggest piece of advice is to be curious because the path isn’t always straightforward.
“I’ve met so many people who came from all sorts of education or work backgrounds,” Susan says. “Even if you don’t see yourself as a typical tech person, there is still so much opportunity. There are lots of ways to approximate education and experience that typical candidates have. You can self-learn and gain experience through side projects.”
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