Bryan Dickinson is not one to shy away from trying different things. While earning his undergraduate degree in Exercise Physiology, he worked as a personal trainer, creating personalized fitness programs for his clients. He had always loved working with people, so he happily took to a career in retail management after graduating from college at the height of the economic recession in 2008. While at Nordstrom, he quickly rose up the ranks from customer service representative all the way to service experience manager.
Despite his meandering career path, Bryan always found himself as the go-to person when his coworkers had questions about Excel spreadsheets or data-related questions. For some reason, he couldn’t shake the memory of a kinesiology class he took in college and one class assignment in particular that required him to compile a lot of data and perform A/B tests.
“Thinking back, I loved that project so much, but I didn’t really understand why,” he shared. Once he started doing research into data science careers, he was hooked.
Now, he’s a senior marketing analyst at REI, an outdoor recreational equipment retailer, and recently won a hackathon, which he participated in with five other Springboard students. The group worked on a project for two months to investigate the factors contributing to water pollution along the Chesapeake Bay, the largest estuary in the United States, stretching from Cooperstown, New York, to Norfolk, Virginia. His team won after they created a machine learning model that could predict water pollution at different locations along the Bay. He says winning the hackathon gave him the confidence to solve problems using data even in areas where he lacks domain knowledge.
“It was super fun and kind of reaffirming that we have the data and the skills and experience to put something like this together, even if we didn’t know much about environmental science,” he said.
All over the place. I was interested in a lot of different things, but I think what impacted my career path was the economic recession in 2008. I had just graduated from college so I decided to stay in retail, and I moved up the ladder pretty quickly. Once the economy rebounded, I became interested in personal training. I enjoyed the customer service aspect of retail and I liked being able to help people as a personal trainer.
What sparked my interest in data analytics was a class project in kinesiology (the scientific study of human body movement) during my undergrad. We had to create different versions of a gold standard test and compare them, which required a lot of data and analysis.
Thinking back, I loved that project so much and I didn’t really understand why. But throughout my career, my peers always looked to me for Excel spreadsheets and other types of very basic data analytics skills. That’s when I started uncovering data analytics and data science and trying to understand what the industry entails, so I took a few classes in data science at UC Berkeley.
I've always had this passion for helping other people and that is primarily what nonprofits do, which aligns with my personal values. There’s so much data being generated every day and the technology is coming to a point where a lot of companies are building really interesting things. Also, a Springboard mentor said something that really resonated with me: As you build a project, think about who this project impacts and the return on it.
I analyzed stop-and-frisk incidents in the Seattle area using open-source data from the state government. I wanted to determine if I could predict the demographic or race of the subject based on the demographic of the police officer in a stop-and-frisk incident. Then I went further to see if I could predict a frisk depending on the demographics of the officer and the subject together. The interesting finding was that there didn’t appear to be a correlation. There were a lot of reports about racial profiling and police using excessive force. My conclusion was that even if the Seattle Police Department as a whole isn’t necessarily racist, an individual can still be racist. So an option for further study is to compare individual police officers to see if they have a trend of stopping more minorities than others. There are also other factors to think about, like what constitutes a stop and what constitutes a frisk, each of which requires a certain type of training. How are officers learning this and what kinds of biases go into that training?
That was a really fun project. I joined the hackathon with a ragtag group of Springboard students. The hackathon was hosted by a nonprofit that wanted to investigate the factors contributing to pollution in the Chesapeake Bay and make recommendations to the local government. One person in our group had a background in natural sciences, so he gave us some insights on how to test water quality.
We did a lot of research and then we started building models and brainstorming how we could answer this question. Eventually, we built upon some data from a previous hackathon hosted a year ago and we used it to predict pollution levels in different areas of the Chesapeake Bay based on certain distinctions by the hydrology unit. We combined weather data, data on pollution from factories, as well as data from other public sources to model pollution for the Bay.
I was considering different avenues for learning data analytics, like getting a master’s degree, self-study, and bootcamps. What drew me to Springboard was chatting with some of the folks at Springboard and learning about the program, the requirements, and the curriculum. The job guarantee and the career services were great motivators for me to give it a try.
I wasn’t hesitant; I wanted to jump in. When I first started, I was surprised and excited about the depth of the curriculum and the things I would be learning. Having limited experience in Python and probability and statistics, I felt empowered as I looked through the curriculum.
I also knew it was going to be a lot of work, but I was excited to know that once I got through this I would have all these new skills under my belt. I also really liked my mentor. He was amazing. It was great having a mentor to answer questions and level set with me. I think it helped me paint that picture of what it is to be a data scientist and what is absolutely needed and what's not.
It was very challenging. My career coach was a great resource for me during the job search. I started job searching when COVID first hit. I remember doing an interview and being told the job had been put on hold. From that point on that was kind of what I heard.
In the interim, I continued to work on projects so I could stay relevant. What really helped was reaching out to folks in the industry and learning from them. I had a set of questions on what they look for, what's challenging for them, what their company is all about, and asking who else I should talk to. That’s how I landed the interview for my current role. The first time I used this technique, I was connected with someone at REI.
But the job didn’t become available until a year later, and my connection had moved on from the company. He didn't get me the interview, but he told me a lot about the interview process and the team. Maintaining that relationship really helped a lot and making sure it was a two-way street.
Leverage your past experiences. While they may not seem like they directly align with what you want to do. Data science touches so many different industries. You may have domain knowledge or know folks who could use whatever experience you had before learning data science.
My top answer would be to leverage the career coach at Springboard. Sometimes you have blinders even when it comes to your own past experience, so having someone to talk to you about your transferable skills is so useful. I don’t think I could have found my current job without the help of a career coach. She was amazing.