Springboard Mentor Spotlight: Kenneth Gil-Pasquel
Meet Kenneth Gil-Pasquel, a mentor for Springboard’s Data Science Career Track.
As a self-taught data scientist, Kenneth Gil-Pasquel knows the value of having a mentor precisely because he lacked one. At the time, platforms like Kaggle and Coursera had just begun to offer free learning resources online, and Harvard Business Review hadn’t yet declared data science “the sexiest job of the 21st century.”
Gil-Pasquel had always loved math, so he earned a Master’s degree in statistical computing, a field of statistics that focuses on computer-intensive statistical methods involving large volumes of data and non-homogenous datasets–or big data mining, in other words.
Now that he’s a mentor, he loves watching his mentees from non-technical backgrounds go from having no prior knowledge of data science to landing full-time data science roles at major companies. Gil-Pasquel acknowledges that switching careers–as most Springboard students do–requires temerity and sacrifice, and he loves supporting students through their journey.
“Some of them have accomplished more than people in the industry who learn data science not because they want to but because they have to,” said Gil-Pasquel.
Tell me about your role at Boehringer-Ingelheim. What does your day-to-day look like?
My day used to involve working with the marketing team to understand our customers better by using advanced analytics, machine learning models, and SQL queries. Now, I work on projects to build tools or solutions for our customers directly by working with engineers and other data scientists.
What does a pharmaceutical company need to know about its customers?
Our customers are the physicians who prescribe the drugs we manufacture, not the end user. When it comes to physicians, we want to understand how they prescribe medications, what area of medicine they specialize in, and so on.
What made you realize you wanted to pursue a career in data science?
I graduated with a bachelor’s degree in statistics. After that, I pursued a Master’s in Statistical Computing, which involved some data mining. That was my first introduction to predictive analytics before data science became an everyday term.
Then, in 2011 or so, Coursera, Kaggle, and other platforms began to emerge, offering free resources online to learn data science. So I started learning from there.
Did you have a mentor while you were learning data science?
Probably not. At the time there really wasn’t anything like that.
As a self-taught data scientist who didn’t have a mentor, what kind of mentor do you want to be for your students?
I try to always reassure my students that I understand that the path they’re taking is not easy, because I did it myself 10 years ago, but it will be very rewarding once they finish. It took me one or two years to learn on my own, whereas Springboard students can become competent at data science in six months.
What do you like most about being a mentor?
Seeing a student go from not really knowing anything about data science to becoming competent and even exceeding my expectations. I always tell them to look back on their first day in the Data Science Career Track and see how much they’ve grown. I think my students should be respected and rewarded for taking the initiative to learn something new.
Are there any barriers to learning data science for students from non-technical backgrounds?
Lots of people have imposter syndrome, and it has worsened in recent years because now people can publicize their successes on social media. Many years ago, people were just teaching each other and trying things on Kaggle. There was no showmanship about it.
But now, people are intimidated. They say, “How could I ever learn that?” It’s like watching a professional skateboarder showing off their tricks and not realizing how much work it took to get there. So I tell my students, we start with the basics and then we grow from there. Focus on you, not the data scientists who have five years of experience.
What is the work-life balance like for a data scientist?
It depends on the company. If you have tons of meetings plus a lot of project work to do, then work-life balance is harder to achieve. But if you work for a company that keeps meetings short and lets you have focus time, that’s a better balance. That way, you’ll have time to learn new things on the side, like reading data science blogs or following topics on Twitter.
In addition to the coursework students complete in the Data Science Career Track, what else should they do to maximize their success?
Improving your communication skills is a huge one. Scientists get caught up on the technical side of things, but we need to communicate our findings to a non-technical audience. Practice doing this with your friends, your parents, your grandmother. It’ll really benefit you when you can influence people and show them the value of data science, especially if you work for a company that isn’t very data-literate.
How has your role evolved over the years as organizations have become more digitally mature?
I used to work with a team of just one or two data scientists. Now I work with engineers, front-end developers, and project managers and the projects have a larger scope. Also, the emergence of digital tools has shown us that we can do data science effectively while working from home.
Why did you choose to become a mentor with Springboard?
People had been asking me how I became a data scientist and I wanted to share my knowledge. So when I saw an opening at Springboard on the Kaggle job board, I decided to go for it. Over time, I started to see how important mentorship was to the people who had made such big sacrifices to switch careers.
Want to become a mentor? Apply here!
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