Alumni Spotlight: Katie Critelli, Data Scientist at Deutsche Bank

Alumni Spotlight: Katie Critelli, Data Scientist at Deutsche Bank

Posted by Ariella Brown

Updated: Nov 29, 2017


Path to Data Scientist

Katie Critelli had spent years doing research when she was considering an academic career. When she decided that she wanted to have greater flexibility and apply her skills outside academia, she recognized that the path of the data scientist was the one she wanted to pursue. To obtain the necessary skills and the assistance in launching a new career, she enrolled in NYC Data Science Academy. Her role in the newly formed anti-money laundering team at Deutsche Bank lets her apply her skills and creative thinking while learning more on the job. We sat down with Katie to learn more about her background, and why she decided to add Data Science to her resume.



Could you fill us in on your educational background before you decided to pursue data science?

I’m from Darien, CT and I went to college at UPenn, where I majored in neuroscience and minored in Italian literature. I spent about 4 years doing lab research as I had intended to pursue an academic career but then I realized that wasn’t the right path for me.

What was your career before deciding to enroll in a data science bootcamp?

After graduating college, I decided that I didn’t want to pursue an academic career and moved to Washington DC to work as a military healthcare consultant for Booz Allen. In the fall of 2017, I moved to New York with the intention of enrolling in a Master’s program at Columbia, but I decided to drop it because I wanted to gain a practical skill, rather than just more knowledge. I found NYC Data Science Academy would help me do that.

Why did you decide to pursue a career in Data Science?

I decided to pursue a career in data science because it seemed like an incredibly important field where the skills I gained could be applied to any industry. Data science combines what I loved about research and what I did as a consultant -- searching for data, analyzing and making sense of it, using it to tell a story, building useful models, and communicating insights to others to inform decision-making.

Why did you select the NYC Data Science Academy over other possible programs?

I considered multiple programs in the New York City area, but I chose NYC Data Science Academy for three key reasons:
1. I wanted to focus on data science as opposed to software engineering or just programming.
2. The program promised students not only classroom experience, homework, and project work, but guidance during the job search and interview process. Plus,  Data Science employer networking events.
3. I immediately liked the team at NYC Data Science Academy when I went to meet them in person.

How was your overall experience at the NYC Data Science Academy? Do you find that it prepared you well for the challenges of real life projects?

My experience at NYC Data Science Academy was really incredible. I felt like I got everything that was promised and much more. The TAs and instructors were knowledgeable, nice, and always available. The curriculum was truly cutting-edge, and the projects involved tackling real-world problems. I met so many other motivated students and learned a lot from going over homework and project work with them. Something else that stood out to me was that the atmosphere was one of constant improvement. We would have pulse checks every Friday to see what students did and didn’t like about the bootcamp. If students had suggestions for ways to improve things, they were taken seriously. I often saw these suggestions implemented within days.

That's great to hear! What’s the main difference between what you knew before the bootcamp and what you know now after the bootcamp?

When I came I had used and Python and other tools but only knew them in a limited way and didn’t even know what gaps I had in my knowledge. Now I know Python and also know what I don’t know. That kind of knowledge is much more useful as it is something I can build upon and apply in a practical way. For that reason, I feel much, much more confident about my ability coming out of the bootcamp.

How long did it take for you to find your current job? How did NYC Data Science Academy help you?

It took me about 1.5 months to find my current job. NYC Data Science Academy helped with the process in many ways, including organizing a hiring partner event where I got my first leads on potential jobs and gained interview experience. I was contacted a month later by a company from the event. Even though I hadn’t spoken to the representative at the event, they were interested in my background and invited me to interview, which eventually led to an offer.

Can you tell us a bit about your new job and your position in the team?

I’ll be working at Deutsche Bank in the anti-money laundering team. It’s a new group whose function is to identify signs of suspicious activity in large amounts of transactional data and to build models based on previously detected cases of money laundering that will flag suspicious transactions. Though I’m working as a data scientist, my role there is somewhat fluid, as it also involves data engineering, web scraping and filtering news from the web, researching, data mining, pulling data from lakes, etc. I expect to learn a lot on this job.

Did you feel prepared going to the job interviews?

I found that I was very well prepared for the interview questions. In the case of Deutsche Bank, which was looking to fill a somewhat creative role, they wanted to know how I would approach a problem. I felt comfortable discussing a lot of different approaches that could be applied to particular situations.

Do you have any suggestions for people who sign up for the bootcamp? 

My advice would be to, first of all, take it seriously. If a TA or instructor says something, they’ve been through it before and really know what they’re talking about. Take advantage of that and try to pick up all the pieces of information you can. The other thing I’d say is to have fun with it. You could have a creative idea or come from somewhere different from everyone else, so even if you aren’t the most advanced person in the room, you shouldn’t underestimate what you can do.


This interview was originally posted on SwitchUp.

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Ariella Brown

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