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VOICE OVER: This is Alumni Aloud, a podcast by Graduate Center students for Graduate Center students. In each episode we talk with a GC graduate about their career path, the ins and outs of their current position, and the career advice they have for students. This series is sponsored by the Graduate Center’s Office of Career Planning & Professional Development. 

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ANDERS WALLACE, HOST: I’m Anders Wallace, a PhD in Anthropology from The Graduate Center. In this episode I sit down with Denys Katerenchuk, who is a Senior Data Scientist at IBM. When we spoke in the summer of 2019, Denys was finishing up his PhD in the Computer Science department at the GC. In this episode of Alumni Aloud, Denys tells us about the similarities between industry and academia, how to turn a mid-PhD crisis into opportunity and the surprising benefits you might reap by going against perceived wisdom when it comes to applying for jobs; specifically, how testing yourself on the open job market can give you a more objective sense of your own skills and abilities.  

DENYS KATERENCHUK, GUEST: My name is Denys Katerenchuk. I’m a PhD student at The Graduate Center and I also work part-time at IBM as a Senior Data Scientist at the moment, at the Chief Analytics office.  

WALLACE: So you’re currently a PhD candidate in the Computer Science department?  

KATERENCHUK: Yeah, my research is natural language processing and AI, machine learning.   

WALLACE: You’re currently with IBM as well as a data scientist. 

KATERENCHUKERNCHUK: Correct. 

WALLACE: And that’s currently a part-time role? 

KATERENCHUK: Yes, so as you know as a PhD student, you also have multiple options. It depends on your personal story. I would say just getting to a PhD is a very challenging task and you think as a prospective PhD student that you can just go get funding and everything will be very smooth. But in real life, quite often multiple things can go wrong. Quite often you will have to teach and sometimes you get lucky and get great funding through your research. And sometimes you have the opportunity to work outside of this academic world. And this is what happened to me. I was lucky to get this job as a summer intern actually, a year ago. And they offered me to stay there part-time and work as a senior data scientist. 

WALLACE: And that was last summer? 

KATERENCHUK: Correct. 

WALLACE: And you’ve been working there since then or did you take a break? 

KATERENCHUK: I actually took a break and it was a good break. I took that opportunity to work at Google in [inaudible] and after having to work as a PM. I moved to Seattle for like three or four months and was working with Google. I think just in general, as a PhD student you have to take an opportunity to explore different companies because many students work really hard till the very end of their PhD and then what happens. They jump at the first opportunity that they come across. And it’s one way to go, but I think you need more of an overview of what’s in the market. If you have the chance to actually take multiple projects at different companies, you can compare the culture, you can compare your fit and just explore different options. And you can find which is right for you. 

WALLACE: That’s a great thing to get that experience before you graduated. Helps you know a bit more about what you want. And it helps your own story with your skills and how you present yourself to the industry. But it also sounds like a lot of work to juggle those…writing a dissertation and working at the same time. Of course a lot of students have to do that. 

KATERENCHUK: Correct, yeah. And honestly just doing a PhD is not easy. So it’s something that you have to keep in mind before getting into a PhD program.  

WALLACE: Now can you tell me a little more about your academic background? From the computer science department, tell me a bit more about your interests and your passions even before entering The Graduate Center, you know, what got you interested in computer science?   

KATERENCHUK: I grew up in Ukraine. And I was fascinated by technology. And one particular thing that I really like is to play games. And I got into playing games on my own PC at home. But then I was kind of questioning some decisions, some technological tools, what made this game possible. So I started looking at the background and I caught myself not playing that much but rather actually trying to understand the goal, trying to understand the story, how everything comes together. And I got really fascinated so since then I decided to go into computer science. And that’s exactly what happened. I went to pursue my undergrad back in the Ukraine. What was interesting was that I think at the very beginning, I didn’t really enjoy academia because I mean if you think about human nature; for thousands of years we’ve been chasing dinosaurs and running away and this new skill that we’ve come across in the past 100 years where you can sit in one place and stare at the screen is actually a difficult skill to master. And this is why many kids these days look at alternative options besides going to university, right.  

But then something kind of clicked in my head and I really started getting into coding. And I really enjoyed writing something from scratch where you have an idea and you create a complete project. That I think is quite interesting. So after my Master’s I decided to join a company back in Ukraine in Kiev where I worked as a software developer for two years. And what was interesting, I felt that I wasn’t challenged enough. So I started to look at different options, what can I do with my life. And one of those was actually a PhD program and this is how I ended up here. 

WALLACE: I love how your interest in video games and sort of transcending the game to see how it’s made, the code and the story-telling, the design of it. It’s something that a lot of people can relate to. Can you tell me a bit more… you started in software development, you’re finishing your PhD and you’re moving in more of a data science direction? Can you tell me more about why you’re moving into data science rather than software? 

KATERENCHUK: I still consider myself more of a researcher, where you work on a new problem that no one has looked at before. It actually comes from being a PhD because as a PhD student you need to write papers, come up with state-of-the-art solutions to different approaches. There is some well-known problem that you’re trying to improve on right. And from my previous work throughout my PhD I’m working my numbers when I work as a research scientist and I think I still kind of relate, even though my title at the moment is Senior Data Scientist, I still do pretty much a research job. Where we’re trying to solve a problem that’s never been solved before and trying to improve on that.  

WALLACE: So it’s similar in ways to what you would do in a PhD? 

KATERENCHUK: Correct. These days, most companies have very strong research departments. And if you look at companies like Google, Facebook, they have well-established research departments to bring work to the masses. And they actually made a big effort to bring academic knowledge to their company. Top managers actually come from academia, they’ve been maybe professors in some colleges. So it’s very fascinating. It’s actually not that different from like real academic life. 

WALLACE: Except the research questions are coming from internally. In terms of solving a company’s strategic goal or problem around a use case for a technology or innovating a technology rather than purely theoretical questions for example. 

KATERENCHUK: Correct, yes. 

WALLACE: Can you walk me through a day, a typical day for instance, if there is something like that in the IBM Chief Analytics Office. 

KATERENCHUK: Because I only work there part-time, my work day is somewhat different from others. I particularly like to focus on producing real results as getting some work done. But usually I would say, a couple of hours per day are usually being in meetings. So our team develops [inaudible] and every morning we have a couple minutes to bring some updates on what you’ve done in the past, what you’re going to do today. General thing. It’s a good idea for setting your goals for the day and keep everyone updated on the work you’ve been doing. It’s a great way to start and I really encourage this kind of workflow in every team. Because the only difference is that we have many stakeholders that we need to update on our progress, to give presentations, your work. And it’s actually really interesting work, again, I think it’s a great fit for a PhD student.  

Because as a PhD student you tend to focus first on the technical side, to create a new approach is like to create technical solutions for current problems right. Write a paper. But also you tend to teach and to give talks and participate in other activities. And I think this is the perfect combination where you have these technical, substantive requirements that you need to solve and then explain it in simpler terms to your stakeholders. What’s interesting is that many stakeholders come from different backgrounds so it’s not necessarily [the case] that they’re on the same page as you. So you need to explain your work in much simpler terms. Maybe give general purpose for a presentation. And it’s somewhat similar to teaching in a way, right. You’ve done a lot of work in computer science but you have to go to back to the basics of computer science and explain as simple as possible to get interest in students and to encourage them to pursue the field. 

WALLACE: I would think you have to show them the impact or what effect it would have.  

KATERENCHUK: It’s a good point. You also have to keep in mind that at the end, you work for an industry right. And you need emphasize the end goal. You cannot just do it for the sake of doing it. You have to have some final goal in mind. Like this is what you’re trying to achieve and this is how it’s going to benefit the company. 

WALLACE: So it sounds like there’s some similarities in academia-the meetings, the research work, the teaching or presenting work. Anything else that would come up at a typical day at IBM for instance? 

KATERENCHUK: I guess similar to every company, where you need to not only do your work well but also maintain great relationships with teammates. Because it’s fascinating in the area of data science, everyone comes from different backgrounds. It’s not the norm in computer science per se that people come from anthropology, psychology, and other different fields and we have different backgrounds. So it’s actually interesting in a way that you can always ask for help. For example, if you work in some kind of behavioral psychology field, you maybe work more with causal models so you can go to them and ask for help. Get their opinion on your work. And part of the work also requires you to bring your knowledge from your area to the rest of the team. 

WALLACE: That’s pretty cool that it’s a very diverse field that people get into from all different kinds of backgrounds. There’re also different kinds of data science. What do you enjoy the most about your job? 

KATERENCHUK: Honestly in my particular job I’m doing a lot of research. And I think research is fascinating. So currently, I’m working on unsupervised problems where you don’t have labels, you have some label and you need to make a presentation of that data. And I’m kind of focusing on neural networks and its honestly to what I’ve done during my PhD life. So I really enjoy this. 

WALLACE: You’re trying to find the pattern that’s implicit in the data rather than trying to apply a category that’s pre-determined. 

KATERENCHUK: Yes, in the industry the scope of data is not that well-defined. It’s quite often that you don’t have access to such nice and clean data. So you need to find it somewhere online or scrape it or be clever. And find some kind of source of data where you can apply your models, apply your knowledge and solve the problem. 

WALLACE: It’s a little bit more in the wild. Pure ingenuity, what can you find, what can you use, and I imagine that must be fun to get to play with these different things and come up with a plan of your own initiative rather than following the thing that’s been handed down. 

KATERENCHUK: It is. And sometimes also frustrating because I would say my expectations were kind of different. I know the scope of the project and quite often I know the problem can be solved in this many months. But if you work in industry you realize it’s not always the case that you can get data in months. So there are more unknown variables in a project so it’s a little bit harder to estimate your work. But also fascinating that you need to be clever in this way. 

WALLACE: Yeah I can imagine getting the data must be hard. But IBM must have a lot of data too. 

KATERENCHUK: As with any company, there is a big emphasis on data privacy. So you’ve heard multiple big companies that have data breaches, etc. And I would say most of the time, it’s not done on purpose. Whether there was a bug in the code, the company in itself as an entity, they try to do their best to deliver the best results to the users. And every company tries their best to make sure that user data is secure and private because if something happens and there is a breach, it will be terrible PR for the company. For this reason, even though you’re part of a company most of the time you don’t have access to user data.  

WALLACE: Oh that is challenging. 

KATERENCHUK: Yes. I think one of the biggest misconceptions is that when you go into industry you think that “Oh these big companies they have so much data you can play with and build crazy models.” In real life that’s not often [the case]. 

WALLACE: What are some of the things that you have found frustrating in… it doesn’t have to be in IBM but just generally working for a big corporation. 

KATERENCHUK: I would say that there are not that many challenges compared to academia for example. I think academia is great but challenging. Because if you want to become a professor, you have deal with funding, you have to find students, you have to teach courses, you have to teach some department management. There are so many tasks that you have to devote your time to and it‘s hard to focus on research. If you go to industry, most of the time you’re kind of sheltered from everything else so they give you the best opportunity to do the work you want to do. So I find that working in industry is much easier than PhD life. 

WALLACE: During your PhD training did you think, “I’d like to go for an academic job” or did you always think about working in industry? Was that always part of your plan? 

KATERENCHUK: No actually at the very beginning when I just joined academia I realized that I did want to pursue academic life for the rest of my life. And I was very happy with this idea up until the moment when something really unpredictable happened. I think it’s one of the least desired things to happen to any PhD student, my first academic advisor, he decided to leave academia and go to industry. And that was terrifying and at this point you’ve been working on this idea understanding that you have a concrete project, that you have all this funding, everything else. And when you know that your advisor is leaving, you have this really emotional like “what’s next” you know. Like what’s going to happen. And honestly it’s quite frequent that professors move around, they might leave, they might come so it’s kind of the way. But before joining a PhD you don’t think about it. And I was terrified at that moment and the first thing that I realized was that I need to find some opportunity to actually find some other options. Like what can I do? And I started applying to jobs for the following summer and was lucky to find and secure a job at Comcast in Washington DC. It was my first exposure. And I really enjoyed this lifestyle when you come in the morning, you do the research, and then you deliver results that are not only as a paper, which is amazing, but you can also do a complete project that is useful for thousands if not millions of users. And they’re using your code, your project, every day and it’s fascinating. 

WALLACE: And it came from this moment of your advisor leaving for industry… you had a fear. You thought, “Do I have a future in academia? Am I going to carry through with this PhD?” 

KATERENCHUK: Correct. At that moment you just hope for the best and prepare for the worst; you don’t know what’s going to happen. Luckily I was blessed to find a new advisor who is amazing and she actually came from the same research lab as my first advisor, so it was the smoothest transition possible. I got lucky in this case. 

WALLACE: Every crisis is an opportunity. 

KATERENCHUK: Correct! 

WALLACE: You realized you really liked the style of work in this sector, what you can do, the pace of the work, got you interested in more of these opportunities? 

KATERENCHUK: Yeah and I guess the most eye-opening thing was that it wasn’t very different from PhD life. Again, if you work in a research department, you read the research papers, there are focus groups that do some fun projects. You’re reading papers, reading books together, trying to improve yourself. It’s not that different from taking classes as you go through a PhD and trying to leverage that knowledge and bring it to your research right. So it’s actually very similar. 

WALLACE: So they have reading groups in… 

KATERENCHUK: Honestly, in every company. At IBM there’s research, at Google. It’s very similar to PhD life. I think like at a PhD student you’re naturally very curious about things but quite often when you join industry you don’t have time, like extra time. So when the company encourages such behavior, like oh let’s have a once per week to read and improve ourselves, everyone jumps at this opportunity.  

WALLACE: So what’s something that you found surprising about working for these tech companies? 

KATERENCHUK: One surprising thing was that you actually get way more productive then because of the environment. You don’t need to focus on teaching, on giving talks, or anything else. You literally come there and work from the morning to the night and you get yourself in this zone where you’re focusing on a single project that you need to deliver. And you do way more than you would have done as a PhD student because you don’t have to deal with other students which I enjoy a lot—teaching—but it takes time from your real work. 

WALLACE: Yeah. Now it sounds like computer science the boundaries between industry and academia are pretty porous. People can move back and forth. 

KATERENCHUK: Correct. 

WALLACE: Do you see yourself ever returning to academia? 

KATERENCHUK: My plan is actually to keep one leg in academia because I love teaching and I think it’s very important to give back to society. And when you have an option to teach students maybe inspire them… I remember my first year as a PhD student I was assigned to teach a course and it was a very terrifying experience for me. Walk into this room and 30 students are looking at you and it’s very nerve-wracking. But as you go through it you understand that you actually uild connections with your students, you build relationships, you understand that your work is to inspire them to be better and more successful in life. I really enjoy that. Actually just maybe, like a few months ago, I got an email from a student of mine. It was just a thank you email saying that because of my work in that first year as an instructor, I inspired the person to go and pursue computer science. And he went to a Master’s in computer science and it was just so eye-opening in a way that this work can actually change so many lives. And you inspire so many students and it’s fascinating. And I would like to keep this option open even though I plan to be part of industry. I’d like to maybe teach a course and have some kind of connection with academia. And actually one of the strongest parts of CUNY and The Graduate Center is that almost every student who graduates from this school, they have great teaching experience. Which is not always the case in other schools. 

WALLACE: You mentioned, you know, that the field of data science broadly speaking is very diverse. People come to it from very different backgrounds. At the same time you find that the research feels like academic research. How do you feel as someone in a PhD program in these companies, do you feel that the PhD is something that your employers or coworkers look at in a different way? Is it something they value you for or is it kind of superfluous and it’s more really about the skills that you bring to the table? 

KATERENCHUK: It’s definitely very important. There are tons of smart people in the world who are just excellent in what they do. And having a PhD is not necessary but having a PhD is a way to show that you can do something that you love for multiple years. You’re not getting paid much, right, you often don’t have weekends, you don’t have nights, you need to work non-stop. And you’ve showed this diligence and devotion to what you do in this particular subject. I think this is what a PhD is about. It kind of proves…you get this paper that sort of proves yourself in a way that you’re devoted to the area.  

WALLACE: That’s really interesting. I mean obviously from computer science you have all these hard skills but at the same time you find it really valuable because of all the things it connotes. This work ethic, this diligence, this commitment to do something which otherwise there’s no real side of that that anyone else would possibly have. 

KATERENCHUK: Right, correct. 

WALLACE: You talked about teaching and how you enjoy it. And that it’s something really useful as far as communicating with stakeholders. It gives you that presentation skill. Are there any other experiences that you had in your PhD that have helped you in your work. Obviously in addition to your coding skills, your programming and data science skills. 

KATERENCHUK: Yeah, definitely! Again, as a PhD student you have to deal with multiple deadlines, like paper deadlines. I remember in my first or second year or PhD school, at some point I was working with another PhD student on a project and we had maybe five days before the deadline. I think we were working on emotion detection at that point. And had this great idea that we’re going to create a better algorithm to identify emotions from speech. But what happened was that we ran into multiple errors, multiple bugs with our code and nothing was working. So those five days I think maybe I slept in total five hours or less. And what’s interesting at the end, I would email at 6am to Dara like “hey, can you look at this problem” and he would email me back so he was staying up at the same time. We were both working nonstop for almost a week without sleep, without food, just had to hit the deadline. *laughs* 

WALLACE: *laughs* 

KATERENCHUK: I think it’s one of the skills you develop as a PhD student that when you have deadlines, no matter what you have to finish it, you have to deliver it. 

WALLACE: Even though it’s painful, you have that camaraderie. 

KATERENCHUK: Correct. Looking back it’s a fun story but I would recommend anyone do that because honestly at the end of it, we were making silly mistakes. 

WALLACE: You talked about how you got your first non-academic job. That was the summer opportunity in DC. Since then you’ve worked for Google and IBM. Could you tell me a little bit more about how you got these opportunities and you know, how you went about it. What was the most useful for you in terms of making the connection and finding the entryway? 

KATERENCHUK: I actually think it’s kind of silly and I would not advise anyone do what I have done but I wanted to test myself and see where I was at. So on purpose I tried not to reach out to anyone, I wasn’t trying to reach out to my friends to send me a reference or get an opportunity. I was literally trying to apply through websites. 

WALLACE: Like general websites 

KATERENCHUK: General websites. Just to see where I was at and to improve my resume, to improve my skills. And to go to interviews that are just very general. You have to compete with multiple other PhD students. And it’s a great way to actually learn where you are right. In your current PhD, what you need to improve. And also you get this fuel that you’ve actually done everything on your own, without asking for help, asking for a reference. But I think it’s a much longer and harder way. So if you have friends with connections in other companies, you should reach out to them, it’s much easier. *laughs* 

WALLACE: Yeah. *laughs* So you got these opportunities by really just applying to website postings. 

KATERENCHUK: Yeah website postings, maybe some recruiting events. Just very general. 

WALLACE: The IBM opportunity also has a few alumni who are from the GC. Was that a helpful thing? 

KATERENCHUK: Correct, yeah it’s actually exactly how I got the job. We have Jonathan DeBusk and Ben Zweig. They were graduates of The Graduate Center and they held a recruiting event for IBM. And I left my CV with them. Couple days later I get invited for an interview in-person. Couple of weeks later I get invited to an interview at their headquarters. I went through that recruiting method. It’s actually a great opportunity because the Chief Analytics Office, the department where I’m working, we have a couple graduate students from the GC.  

WALLACE: So there is value in recruiting events at the GC. 

KATERENCHUK: Yes definitely! On campus they are very helpful, very beneficial. Because companies are literally going to the school to recruit people right. It’s not just what they advertise themselves, they are looking for someone to hire. It’s very advantageous. 

WALLACE: In addition to what you received in your PhD training, did you have to do any other kinds of training outside on your own to get these jobs or to position yourself for the data science opportunities that you wanted? 

KATERENCHUK: Coming from computer science, it covers most required skills. You get to learn some quantitative analysis skills, you get to learn some technical skills. I guess in a way you just have to be innovative. As I was saying earlier, in addition to the data you get, you have to be a little more creative. And honestly a PhD is very helpful. Like at the moment looking back, most of the skills you acquire throughout your PhD, they’re very applicable in industry. Data science is kind of new and not really clearly defined. And every company has their own interpretation of the job. But in general, you have to know how to write code, learning Python is very important, learning some algorithms is very important, having a background in quantitative statistics is important. Right, so much of the learning courses are important because now you have the ability to analyze the data and build a model that can solve some problems. And put it all together to deliver as a product. 

WALLACE: Now that you’re finishing your PhD, now that you have a really solid footing in industry and data science… what do you know now that you wish you knew when you were first starting out in graduate school?  

KATERENCHUK: I would say the most important thing is to have balance in your life. Don’t forget about your friends, don’t forget to exercise, keep yourself motivated. Because there are so man y students throughout my PhD life that have been working so hard they get depressed, they quit the PhD. Maybe found another opportunity. And it’s not necessarily a bad thing, I think it always works out well. But keeping this balance to motivate yourself, keep going, if you crash yourself over a single deadline then you have to spend 2 months to recover from it. I think just the balance in life is very important.                

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WALLACE, VOICE-OVER: That’s a wrap for this episode of Alumni Aloud. I want to thank Denys for coming on the show to share his experiences transitioning out of PhD life with our listeners. Remember to stay tuned for more episodes of Alumni Aloud, published every two weeks during the fall and spring semesters. Subscribe on iTunes and you’ll automatically notified of new episodes. Also check our Facebook, Twitter, and career planning website at cuny.is/careerplan for more updates from our office or to make appointments with our career counselors. Thanks for listening and see you next time!  

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