She helps clients to define their problem, but also to solve it. And that’s exactly why NL News of the Week Elif Saraçoğlu likes her work so much. She is a data scientist at SAS and feels completely at home in the company, even though she actually started in a completely different branch of sport.
Elif is Turkish, but moved to the Netherlands years ago. She also worked at SAS in Turkey, although she actually started her career with a completely different plan. “I studied mechanical engineering, but switched to data science. The girls in my class, and there weren’t many of them, mainly wanted to do something with airplanes, while I wanted to help people more. That’s really in my personality. In a personality test, I always end up in the middle, with a clear emphasis on helping people. A job really has to fall on that spectrum for me to feel happy.”
Now she is a consultant at SAS. A company that helps customers with innovative software. She can now fully help people. “I have meetings with customers to discuss the problem they would like solved. I then look at how we can solve that from a data science perspective. It happens that customers are not able to properly explain their problem, let alone solve it. So their story needs to be looked at from different angles in order to ultimately be solved with processes and software.”
An example of such a problem: A bank where Elif worked was digitizing their credit risk process. “If you implement that in a bank, you will have to deal with many different departments. So we had to make all kinds of combinations and help automate the whole process. After all, there are many wishes involved in all those different processes.” That is exactly what Elif feels comfortable with: “I like data science because it solves problems. You are working on a solution that will improve someone’s life.”
Elif would like to see more women working in data science. Although she also understands why it is a field that does not look attractive at first. “When I was little, I had a kind of math anxiety. However, when a teacher took me into equations, with X and Y, I started to understand and love it. I knew pretty quickly that I wanted to do something with robots and AI. As a kid in the ’90s, I grew up with all that science fiction. I always loved the Jetsons. Partly because of this, I started with mechanical engineering. A very nice field of work. Still, it would take me years to get where I wanted to be (ie researcher or professor). I left my master’s degree in business analytics.”
She could now apply everything she had learned before. “Only now you see the results in six months to a year. I’m not impatient per se, because I like to fix bugs. Then you need a lot of patience. However, I do like to see something in motion. There has to be action and preferably a little fast. If a robot or system enters a test phase in five years, yes, that will not make me happy,” says Elif. Who says he is good at process design (i.e. building models) and likes to keep the bigger picture in mind.
However, there is also something she likes to do a little less. “Like any data scientist, I am not at all interested in cleaning up data. Cleaning your dataset is a mess, but you have to do it to make sure your models are correct.”
In data science there is also a lot to do around data that ultimately turns out to be biased on the basis of gender or ethnicity, for example. This does not bother Elif in her work, “because I currently work in a factory.”
But, has she ever been bothered by it herself, prejudice or discrimination? “I am very lucky: I have never really experienced anything like this in my work environment. I always work with strong, powerful women. It was different when I was a student. There were 300 engineering students, 20 of whom were women. At one point I went on an internship somewhere and I heard other students say that I only got that internship because I’m a woman. That’s not nice to hear.”
Elif had to fight for her spot. With a father who is an architect, her friends and family actually wanted her to study design and then also architecture. “He really didn’t understand why I chose mechanical engineering.” She chose her own path and it turned out to be a great choice. “I feel so at home here. I do think that data science needs more women. If you’re not into it yet, don’t be afraid of coding or if you have math anxiety, it’s about thinking and identifying a problem.”
Moreover, data science is also very dependent on the field in which you do it, says Elif. “There are so many industries to work in: every industry is currently involved in data science. If you don’t like it somewhere, take a look at data science in another sector. That is one of the reasons why I work as a SAS consultant: I can get a taste of many different sectors and I feel very welcome there. I think that if people are not having a good time, people are too quick to think that it is the profession, rather than the sector.”