Nick Lenczewksi is a prime example that people don’t have to be solely left-brained or right-brained.
After majoring in math in college, he became a multi-skilled linguist teaching English, doing copywriting for various hardware and software companies, and serving as a Chinese interpreter and translator. But after a few years, he felt a hankering to return to his math roots.
An initial Google search told him that with a degree in mathematics, he could land a lucrative role in big data. Shortly thereafter, he decided to enroll in Springboard’s Data Science Career Track. Now he’s a data scientist at Ovative Group, a performance marketing agency based in Minneapolis.
“It feels great to be in a problem-solving mindset at work,” said Nick. “A mathematics background isn’t required to learn data science, but it is a foundational piece of it and makes it easier to learn the other pieces that are built on top of it.”
I started as an English teacher and then I did translation and interpretation in Mandarin. I enjoyed it, but it didn’t give me the opportunities I wanted. In college, I majored in math. So I literally just Googled what jobs I could do with a degree in math. I found big data, which led me to machine learning and data science. That’s how I discovered Springboard, too.
It didn't pay very well. There wasn't room to move up unless you were really, really talented and willing to move to New York or Washington D.C., where you could be a conference interpreter. It seemed like there weren't a lot of opportunities to do more with my language skills and I wanted to do something math-related.
I’m an analyst in the machine learning operations department. I’m responsible for onboarding new clients to our marketing measurement product. Ovative Group is a marketing company and one of the services we offer is a tool to measure the impact of marketing campaigns so the customer can make better decisions about how they want to spend their marketing dollars. My role is to implement the machine learning model and get new customers up and running.
I'm still exploring, but I am interested in machine learning and all of its applications, whether it’s working with marketing data like my company does or natural language processing. It’s an amazing field that can be applied to such a broad spectrum of industries.
I liked the fact that there was a job guarantee and a career coach who would help me find a job. It was important to me that I would not only learn technical skills but find a job I like.
I thought it was very good. I considered learning data science on my own, but the problem with that is not knowing when enough is enough. My mentor was really helpful in guiding me in the right direction.
My primary mentor was Hobson Lane [co-founder of Tangible AI]. I liked that Hobson used the Socratic method to teach–asking me lots of questions, trying to get me to think and point me in the right direction. My mentors were the first professional data scientists I ever spoke with.
That's a good question. One thing Hobson told me was not to be afraid to take a data analysis job because then I could use data science techniques on the job. I got a data science job, but it's still good advice.
My project had two parts to it. The first part of it was language detection. The model was fed 22 different languages and it could predict what language it was reading. That was fairly simple.
The second part of my project, which my mentor suggested I do, was to have the model predict whether a sentence was the first in a paragraph or not. For this model, I focused on Chinese. The model had to interpret punctuation, which tells you where sentences start and end. I also had to build a package that could tokenize the Chinese language correctly, because Chinese doesn’t use spaces.
Yeah, I did. I discussed it during at least one of the interviews for Ovative Group, my current employer.
The career coach was a huge help. I applied to about a hundred jobs and did not hear back. I thought maybe something was wrong with my resume or LinkedIn profile, or I was applying to the wrong jobs. After talking with my career coach, she reassured me I was doing the right things and to just keep applying. That was really eye-opening, because I didn’t know that it takes 200-400 applications before you start getting callbacks for interviews.
Make sure your LinkedIn profile and resume are top-notch. Have your career coach and mentor look it over. Focus on just applying to lots of jobs. I applied to about 20 jobs per week, and you’ll eventually start to hear back from some employers.
Springboard did a great job preparing people for job interviews. I did five interviews for my current job.
The first was a phone screen where they asked questions like “Is this pay range okay?” and “Is this the kind of job you’re looking for?”
My second interview was with the director of data science, and the questions were half behavioral, half technical. It wasn't too in-depth or anything. I had a third behavioral interview with the hiring manager, and I discussed my capstone project with him.
The fourth interview was a technical interview and I completed a take-home challenge–exactly the same format as what Springboard teaches their students, where you follow the data science method to do data analysis, build a model, and talk through it at your next interview. The final interview was with the CEO, and it was a behavioral interview.
Right. I think I was really lucky because Springboard prepares you for the technical questions. I had written out about 50 common interview questions and answers. My advice is to figure out the common questions and practice responding to those.
That's a tough question. It would be either the mentors or the career coaching. Both were really helpful. I learned so much by talking through my capstone project with my mentor while I was working on it.