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Marina Kingsbury found herself in a situation familiar to recent PhD graduates who are looking for jobs in a dwindling academic job market. Post-2008 financial crisis, the number of full-time teaching and research jobs in her field were declining, while at the same time departments continued recruiting and graduating PhDs. 

Marina’s race to stay competitive, however, was even tougher. The forty-something-year-old mother of three couldn’t publish papers as fast as her colleagues, which was a key metric in her field. “I just couldn't make it because I had too many gaps on my resume. I had the kids as I was working on my dissertation, so it took me longer than normal to finish it. And academia is really harsh. When they see that you haven't published for a while, or it's taking you so long to finish a dissertation, then you’re not as lucrative a candidate,” she shares.

At that point, many would have suggested that she shift into a business career — with a PhD, she ought to be a top-notch candidate, right? Yet so many companies saw Marina as overqualified. “It's really difficult to find a job,” she says, “I have all these credentials, and they’re looking for experience with these resume engines, and my experience is very specialized.”

But those who seek shall find and luckily Marina was no exception. She learned that her acquaintance used to be in a similar position and made the switch to data science. “And I thought, that's an interesting topic, because political science is increasingly more quantitative, we do a lot of modeling and we're taught to do quantitative research,” she shares. Around the same time, she spotted a data science job opening for YouGov — a global public opinion and data company that was looking for someone to crunch the numbers for the 2016 election. She managed to contact the person in charge of hiring, who, as if on cue, had walked Marina’s path before. “He was kind enough to come back to me in a personal email and say, ‘Hey, I'm gonna say I was in the same boat as you were. I'm also a political scientist and refugee from academia.’” What’s more, he described the skills he was looking for in a candidate, and recommended a book on coding for Marina to use as a guide.

From it, Marina learned that data scientists use two programming languages: R and Python. She was already a pro at R, as it is widely used in academia, particularly by political scientists. It meant she was already halfway there; she just needed to be proficient in Python.

Marina started looking for online courses on her own, but quickly realized the things she was taught didn’t line up. “When you take these courses, there's a problem, and you solve it. But at the end of the day, when I finished these programs, I felt like I was learning things in sort of silos, and I wasn't really ready to be doing it on my own.” Looking for alternatives, she joined a local Women Who Code group and came across Practicum’s ad on their Slack channel. 

With a family and a small consulting practice, it was important for her to know how much time it would take and whether it would be worth the effort. Marina had Russian roots and already knew about Yandex's reputation - the company that created Practicum - so she decided to sign up. 

Developing another perspective

The Practicum Data Science course consisted of several two-week-long sprints, each with a strict deadline. But with a PhD under her belt, Marina’s deadline management skills were unparalleled. What’s more, the deadlines helped her keep going and stick to the plan. “The sprint deadlines are quite tight. And that really drove me crazy. Because I had so much to juggle on my own — the kids, work, and family. But at the same time, it kept me motivated. It wasn't like, ‘Oh, it's okay, I'm going to finish it tomorrow!’ Deadlines really helped [me] to stay on track.”

Another thing that kept her attention was the context of problems she had to solve within the sprints. Practicum put Marina in an environment similar to that of real IT departments and showed her how the problems she was solving were related to actual business processes. It set Practicum DS course apart from other bootcamps she’d tried, as it enabled her to understand where her work would stand in the general workflow for her future employer. “Practicum was like, ‘Hey, this is the company and this is what the data looks like. Based on this situation, this is the kind of problem you'd be solving. This is what it could look like, and this is what you will be expected to produce,” she says, “They prepare you for a real career. They give you a taste of what your professional tasks will look like.” 

Due to her academic background, Mariana was used to sourcing data, analyzing it, and presenting her findings at a conference or in a publication. Coding, though, was a different matter. “In academia, we work with different statistical packages,” she explains, “So, in a way, it was easier for me to transition to data science, because I have the skill and the discipline to work on projects and understand data. I primarily struggled with the technical side of coding.” That’s where the course’s Slack channel was invaluable: whenever she felt stuck, Marina could either post her questions or figure out a solution based on the discussion between her peers and tutors.

Marina mentions that though it’s easy to get discouraged by elaborate code written by others, just remember that your task is to produce the simplest working code in the shortest amount of time. “I’m in my 40s, and I'm transitioning to a completely different field, and it's terrifying. I hope that I can be a role model for somebody who's in the same boat.”

Getting her foot through the door

Is it easy to find a job with brand new skills in an unfamiliar business world? Well, no. But Practicum combined with Marina’s researcher mindset certainly makes the process smoother.

For one, she knows what she wants to do. She wants to join a smaller company where she could work with the entire cycle of data. “I wanted to do that on my own, because in bigger companies, there are narrowly defined roles. I wanted to understand how it's done.” She wants to test out data analytics, thinking it would give her a better idea of how things in the industry work. 

“I listened to a talk that Women Who Code did, and one of the panelists said: ‘look, if you're transitioning to data science, it is sometimes okay to start with data analysis. Sit down, start with the data analytics job, and then work your way up into the data science role.’ And that to me was very important, because that broadened my range of searches. It is true — you have to get your foot in the door. Then you're able to look around, figure out what you’re lacking and what else you need to do to build your portfolio.”

However sometimes, finding your perfect role and company takes time, so Marina will keep searching. 

Is she satisfied with her career shift in the end? We certainly think so. “It makes me happy. I feel like a magician: you get this really messy bunch of stuff. And then you manipulate it, and you get really frustrated with it. You spend hours on it, but it's not working and you're getting nowhere. And then you finally get it done. And then out of all this mess, you produce something beautiful. You're able to find some insights, and you go — ‘Hey, I just pulled a rabbit out of a hat!’” 

If you’re interested in pursuing data science or analytics, Practicum can help you get started

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