Hayley Caddes traveled across the country from San Francisco to New York to pursue her education and now works at her dream job. While she was planning her career in chemical engineering, she realized how central data science is, so she decided to switch gears and devote herself to that field. After gaining the skills she sought at the NYC Data Science Academy bootcamp, she obtained the Data Scientist job she wanted at a startup called BerlandTeam. Currently, she works as the Associate Director (NYC) at The Knowledge Society (TKS).
- Could you fill us in on your background before you decided to pursue data science?
I’m from San Francisco and started my undergraduate studies at the University of Puget Sound. I earned my BS in chemistry there. I wanted to expand my knowledge beyond chemistry to physics and math and so enrolled in Columbia University where I went on to earn both a BS and MS in chemical engineering.
- What made you decide to pursue data science?
After I started the job search process, looking for research positions in bio-engineering labs, I realized that the requirements of the job called for more of a data science background than I had. I didn’t really know exactly what skills were needed but noticed they included R, Python, statistics, data analysis, and data visualization in the listings for chemical engineering positions.
- Why did you choose the NYC Data Science Academy bootcamp?
I wanted a course of study that covered both R and Python and that offered data visualization and analysis, as well as machine learning. I did a lot of research on the available programs. I read reviews and blogs about the different data science programs. I found that NYC Data Science Academy not only covers the subjects I wanted, but they also had a very high job placement rate.
They’re very confident in their ability to help you find the position you’re looking for - and that’s very helpful, especially when you don’t really know the landscape of the data science research industry. That combination of teaching two languages, the highest reviews, and the highest job placement rate made the NYC Data Science Academy’s bootcamp the obvious choice for me.
- How did you find the bootcamp experience?
The first few weeks of bootcamp were like regular school. Then it ramps up with projects. I liked that added challenge. With so much to learn, you have to learn how to manage your workload and how to think in two languages. I’m a very math-driven person but had never touched statistics before. It’s a different side of math that was completely new to me. They also have a lot of resources here that can help you hone a skill. If you’re willing to put in the work and ask for help, you can succeed. You get out of it what put into it.
- How did you find your fellow student?
Everyone in my cohort was very hardworking, very smart, but also very nice. It was a really cool, collaborative environment.
- What did you find most interesting in the bootcamp?
Machine learning was interesting. Honestly, coming into the bootcamp, I didn't know much about it, beyond the casual consumer level. So I was familiar with it terms of a newspaper or Wired article but not on the level of someone who engages with the data science community.
- Which part of the bootcamp was your favorite?
Definitely the capstone project. We had total freedom with it, no constraints on the assignment other than “show us what you learned in bootcamp.” The problem we addressed was if you’re thinking about going to a concert, should you buy the tickets earlier when they are a full price or wait for prices to drop? We got to use the Amazon Web Services database using S3 and Redshift. That was a great experience to have because one of the first questions I was often asked in interviews was: “Have you worked with AWS? Have you worked with Redshift?”
- How was your job search process?
My job search began with working on my resume for a few weeks. Once people called me and asked for phone screens, I would do a phone screen with a company. Usually, after that, they would give me an assignment. These assignments generally worked seamlessly into my workload at the bootcamp.
I was able to do most of them using the skills I learned there. But it was also great to have instructors on hand to work out the code and work out the kinks and offer guidance on how to present the data’s story. That’s what made a big difference so that people reviewing the assignments could say: “She has clearly learned how to take a data set, and she not only can visualize it and analyze it, but she can say something about it. She can generate insights.”
- Can you tell us about how you came to work at your present job?
What I was looking for was something that was small, something where I could practice all the skills that I had learned. I found just that. I was applying for a million jobs and then found an opening for a junior data scientist position. The thing that set me apart from the other applicants , as I was told after I was later told by the one who hired me - was my assignment. It was a web scraping assignment. I did some really good work there. I was able to tell the story with the data.
- What are you doing now?
I’m working in a really small startup. I’m only the twelfth employee at BerlandTeam. I currently work at TKS, a human accelerator for innovative people which is around 13-17 years old. They work in areas like Brain-Machine Interfaces, Quantum Computing, Artificial Intelligence, Augmented Reality, and Genetic Engineering. The program has been in Toronto for 2 years, and I'm helping launch its first year in NYC. It’s really exciting! It was exactly what I wanted to do.
I don’t think I would have been this happy at a chemical engineering job, which is why I did the program in the first place.
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