Meet Vera: the Superhero of Software Engineering
This post is part of a weekly series in which we get to know Sifties.
Vera Dadok is a Software Engineer who has been with Sift Science for three months. If she could be an animal, she would be a dolphin because they’re great swimmers and know how to have fun.
What superpower would you want to have and why?
Teleportation, but only if I could take someone with me — travel is better with friends. We spend so much time traveling from place to place, and time spent in transit can often feel like a waste. No one really enjoys being at the airport, do they?
If a movie was made of your life, what genre would it be and who would play you?
It would be a comedy, maybe one of those comedies where everyday things are happening but it’s still funny. I would want Tina Fey to play me. I’m not saying that I look like her, but I think she’s awesome and hilarious.
What was your first car?
A used forest green Honda Civic. It was a stick shift, which I barely knew how to drive off the lot, and had a dent in the nose. Actually, the dent may have appeared after I bought it… But the upside was that it made the car (i.e. me) look tough, and people would move over on the freeway for me. I named it “Car.”
How did you get interested in engineering?
I started out majoring in math, but realized that pure math wasn’t for me. I didn’t feel it was practical enough. Around that time I was talking with a few students in the Mechanical Engineering department, and they told me about the cool robotics projects they were working on. I found myself wanting to understand how tech works, and to build something myself.
Speaking of engineering, why did you transition from mechanical engineering to data science/machine learning?
My major in graduate school was control theory, which has close ties to machine learning. I found myself more and more interested in machine learning, taking a variety of related classes at UC Berkeley, and in my dissertation leveraging techniques from machine learning to relate parameter pathways in a mathematical model of the cortex to EEG and ECoG measurements. I was thrilled to find an opportunity after graduating to work in data science and machine learning in an industry setting. And now I’m excited to be fighting fraud with machine learning at Sift Science!
