Combining Computer Science and Business Strategy at Facebook | Interview with Michael Keaton

Combining Computer Science and Business Strategy at Facebook | Interview with Michael Keaton

We met with data scientist Michael Keaton to discuss his career journey, the future of the field of computer science, and the value of formal education. Enjoy!

Michael Keaton describes his path from an undergrad at Princeton University to a Data Science Manager at Facebook. After teaching himself how to code, he led a start-up in San Fransisco as a product manager. Now he applies data science to market strategies, working with companies to combine consumer information and business. With data as one of our most valuable resources, Keaton is at the forefront of data as a commodity.

Keaton discusses the benefits of learning computer science in a formal education setting compared to learning it from real-world projects and applications. He also emphasizes the benefits of a blended career path that includes formal training and practical work experience. He comments on the future of the accessibility of computer science and discusses the importance of developing a breadth of skills. Finally, Keaton gives practical advice for students who are searching for a career path and the best ways to increase marketability with skills in computer science.

I'm in favor of blended career paths, where you do a combination of formal training and either work experience, or a lot of schools will have externship programs now or co-ops, I think those are really valuable.”

Karina’s Interview with Data Scientist
Michael Keaton

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Interview Transcript

(Editor’s Note: The following transcript has been lightly edited to improve clarity.)

0:00:01.2How did you get into the field?

Karina: Hi, my name is Karina Macosko from Academic Influence, and I am here with Michael Keaton, and we wanna hear how you got into your field and how you began working at Facebook.

Michael Keaton: Thank you so much for having me on, Karina. I would say that my path to data science is a bit circuitous, and it started in college. I thought I wanted to be a chemical engineer, and so I had studied Chem E for nearly four years at Princeton. And around the time of my Chem E design projects, we were doing a lot of work on software simulations of chemical processes, but also the related business and financial outcomes, and I was really, really interested in that ’cause chemical engineers spend a lot of time optimising things. And I found myself a little bit more actually drawn to the software than the chemical processes. At that particular point in time, we were using some software called Aspen. But I was fascinated by the idea of being able to use software and to simulate many thousands or tens of thousands of times over how we might build this plant, what the financial outcomes might be. And that kind of really seeded the path for me to pursue software technology, maybe more than chemical process technology. And I actually taught myself how to code and helped build a few applications in college and thereafter and that kind of sent me down a path towards being interested in building products, technology products, using software, and I went to grad school to become a product manager. And I did become a product manager for a while, actually, and led a product group at a start-up in San Francisco.

What I found myself missing on the product side though, was that high-level business strategy, and I found myself kind of craving ways to plug into those conversations, and so I ended up being recruited to a group at Google that was using analytics in a new and emerging space that we called quantitative program design. And so we were still using software technology to a certain degree, but we were actually kind of applying it to large-scale business problems rather than just building kind of bread-and-butter tech products. And that led me to the world of applying data and data science to sales and go-to-market strategy, and that’s what I’ve spent maybe the last seven years or so doing. And I moved over to Facebook a little under three years ago to lead a data science team that’s specifically doing that work to use everything that we can collect about interactions with customers and what solutions and products they need to actually drive go-to-market strategy.

0:03:33.4Were you self-taught?

Karina: Wow, that is fascinating. And so…

…is what most of what you use in computer science self-taught or after you went to grad school, did you take computer science classes?

Michael Keaton: I’ve taken a little bit of CS myself. I would say, like the bread-and-butter coding, I largely taught myself. Now, I manage teams, so I’m doing a little bit less programming on my own, but I would say it was a combination of self-taught and then some targeted mentorship in those areas.

0:04:05.2Should you major in computer science?

Karina: Wow, that’s awesome. And so, for other people, if they wanna go into something, maybe within computer science or within applying computer science to business like you are…

…do you think it’s important for them to major in computer science in college, take those kind of classes? Do you think that would have been easier to kind of get where you are if you had done that, or do you think that they can major in anything and still be successful?

Michael Keaton: That’s a really good question, Karina, and I’ve spent a lot of time thinking about what would my advice have been to my 18-year-old self in choosing a major. I suppose on the one hand, if I had figured out a little bit earlier that my interest was more on the software side than the chemical side, chemistry side, perhaps it would have helped to major in computer science or related fields. At the same time, I think chemical engineering is a really valuable foundation, and it teaches you a lot about building really robust and scalable processes and thinking from a systems design standpoint. Those are actually concepts that translate to software engineering and to a lot of the work that I do today. So I certainly don’t feel that it’s not a useful background. I think for folks considering a career in data science or a software adjacent field, I definitely think some exposure to the fundamentals is helpful. I don’t think that means you have to major in computer science, but I think either through formal coursework or there is a ton you can do online these days and pick up from really high-quality resources, there’s great mentorship programmes and so forth, I think some exposure is definitely helpful.

0:06:03.2Should you go straight into the workforce?

Karina: Wow, that’s so interesting. And so, also on the flip side of that…

…do you think somebody who goes and majors in computer science can go straight into the workforce, or do you think there’s a lot that you have to learn on your own about programming that they’re not gonna teach you in a CS class?

Michael Keaton: That’s a really good question. I think the world has moved towards more practical CS degrees, say in the past five or seven years. However, there’s definitely a gap between the theory that we study often in formal coursework and what is required on the job, so...

My perspective is a blend of academic training and practical experience really helps. So while my academic training was chemical engineering, then I actually spent a few years working before I went to grad school for product development. I felt I really got a lot out of my Master’s program, because I had had some work experience, and you really start to tune, "Okay, these are like the gaps in my skill set, this is what I’m going back to school to learn, this is what I’m hoping to come out with." And I felt I was much more directive and disciplined in what I took on in grad school as a result. So I’m in favor of blended career paths, where you do a combination of formal training and either work experience, or a lot of schools will have externship programs now or co-ops, I think those are really valuable.

0:07:39.0What’s your goal?

Karina: Well, and that is great advice to a lot of the young people who watch these interviews, is that you don’t have to know what you wanna do going into college or even leaving college, because sometimes that practical work can be what fuels your career. So yeah, I think that’s really great advice.

And so now, working for Facebook, it’s one of... It’s... Could argue the largest company right now. What is it that you’re doing, and what’s kind of your goal with that going forward?

Michael Keaton: Yeah, I’m kinda fascinated by the field that I’m in, and I really like solving the kind of problems that we saw. We get to work at massive scale. At the size of company Facebook is, there’s obviously millions of customers, many questions you can ask about those. And as a result, fundamentally those are really like data science questions almost. I’m someone who... I find there’s a lot of value in breadth from a career standpoint, and I think it’s... Even I’ve spent seven years in some flavor of go-to-market strategy, I’ve looked at that space in the same business, Google and Facebook are not fundamentally different business models, but looking at it through different functional lenses. So even though I manage a data science team now, prior to that I’ve done more traditional strategy and ops program management, and also I had had some experience on the product side.

So I’m viewing my... The experiences that I’m building now is helping to broaden my functional experience while still staying in one industry and the same broad customer segment. And I think there’s a lot of value in that. And I think that by building that breadth, it kind of sets you up to do a lot of interesting things in the long run. Like if for example you want to run a business unit in the future, having done five or six of the critical functions for that business unit, maybe run a technical team, maybe run a team that bears a quota, maybe run a strategy org, maybe run a business planning and operations team, having those types of experiences, I think, sets you up very well to build a long career in that space and to become a really well-rounded leader.

0:10:13.1Are you a better leader?

Karina: Well, that’s awesome.

So you think that the variety of stuff that you have been involved in, chemical engineering, and then your Master’s program with business and computer science, has really set you up to be a better leader or more valuable in whatever field?

Michael Keaton: Yeah, I intend to stay in this field, broadly speaking. I think small, medium-size businesses and go-to-market strategy in that area is something that I care a lot about. What I mean to articulate is I’m building breadth of function, I haven’t only... I’ve done things beyond just data science and being a data science leader. I’ve done more bread-and-butter strategy and ops and... As well as product management. So I’m... I’ve kind of looked at this space through a few different lenses. And I think that type of breath then, if you wanna run a business unit overall in the future, that’s very valuable experience. If you wanna start your own company in the future, these are [0:11:22.8] ____ So I’m a fan of breadth actually over time, and I would say the depth piece I’m building more of is on the people leadership side.

0:11:35.0Where is computer science headed?

Karina: Oh, that’s awesome.

And so beyond just your field, computer science in general, where do you think that’s headed? Because right now it’s one of the biggest fields, it’s kind of been incorporated into everything, business, chemistry, everything. And so right now, you can kind of get into it just having self-taught, like you were, with minimal training. And where do you think it’s headed? Do you think it’ll continue to be a very accessible field for people so that maybe even if you don’t get a college degree, if you’re very well-trained and self-taught, you can get into it? Or where do you think computer science is headed?

Michael Keaton: Yeah, I think there’s a few really interesting trends. I’m definitely bullish on the space. You’ll see headlines and top investors talking about software eating the world, I kind of do believe that is happening in a lot of industries. I think there’s something... There’s two dynamics. One is the rise in importance of the big tech companies that play a really critical role in our lives, whether it be on our devices, how we connect with others, how we communicate, how we get our groceries and so forth. And that trend I see continuing, and we will need more and more folks that can write code, can program, can lead those teams in that part of the economy. I also think there’s another trend where traditional industries that you wouldn’t say, these are CS industries or sort of historically places where data science has played a role.

Though places like healthcare, aerospace, defense, these types of industries are really being transformed by the same digital times and those types of companies are building out their own data science teams, you see them having Chief Information, chief technical officers play a stronger role in the influence of the C-suite. And so I think even if your field or your area of focus is not in what we might consider software technology or traditional IT, a lot of these other industries are applying digital tools and that in similar ways, and so even leaders in healthcare and some of these other industries, I think would benefit a lot from exposure to analytics techniques that we’ve been using for a longer and kind of the traditional software technology and IT companies.

0:14:26.8How do you become marketable?

Karina: Well, yeah, I think you’re absolutely right. I mean CS is being... Every industry can benefit from computer science, just like digitalization. I really do think that’s where kind of our society is headed. And so just closing out advice for people who maybe have seen the same trend you are, where computer science is really taking over healthcare, biology, chemistry, any type of field, and they’re trying to decide whether they should minor in computer science in addition to whatever field they wanna go into, or whether they should find a mentor who can help them learn computer science or just try to learn it on their own.

What advice would you have for them, is the best way to become marketable in computer science and go into one of these fields that’s not traditionally computer science related?

Michael Keaton: Yeah, it’s a really good question. I sort of have three broad strokes of advice for folks thinking about those questions and students in school. One is that... I think even though I’ve talked about becoming more breadth oriented as my career progressed and enjoying that and liking the value, I think that both paths are totally valid you can build breadth of experience, but be looking at the same sort of industry or set of customers, or you can become a subject matter expert, I think that’s something that’s important to figure out early on, right. Do you wanna go really deep in one field and be a machine learning expert on a certain type of modeling and maybe get a PhD, right, that would be an example of becoming a really deep Smee, versus my career has been more about building breadth.

I think both can lead to really successful careers, but understanding early on whether you see yourself as a depth person or a generalist, seeking more breath, that can be very helpful in orienting classes you take, internships that you take on and kind of how you structure those career decisions. Two is... I do... To your earlier point, I think exposure to the fundamentals helps a lot. So it doesn’t mean you need to major in CS, it doesn’t even necessarily mean you’re taking a one-on-one course, but I think having some understanding for the how it works, either through self-taught or formal training does help regardless of the industry. And then I think third is, just maximizing the number of kinda conversations that you have, and I know that academic influence provides a very scalable way to do that, right, students can watch these interviews with a lot of leaders in those fields. And I think my kind of closing advice to students is that you all have a secret weapon today that we often forget when you’re in undergrad or even in grad school, and that secret weapon is that you can reach out to essentially any alumni or folks in the extended network, really good ways to do this, email, LinkedIn and so forth today, and I think it goes something like, "Hi, my name is Michael, I am a junior, senior at Wake Forest University," or wherever you’re studying.

"I’m really interested in data science with an application on," say, healthcare in this case." I see you’ve taken a number of steps on that path, and I’m just really interested to hear more about what you’re doing and what drew you into that field and just get a little bit of advice." You’ll find the hit rate on those types of reach outs is really, really high. And that sort of secret weapon kind of goes away when you’re outside of the academic spheres and more in the working world, but that’s something I really encourage students to take advantage of, because while you may not be able to control kind of exactly which job applications or internships work out, something that’s well within your control is the number of those conversations that you can have over the course of your academic career. And I think just hearing more and more folks understand their paths, likes, dislikes, and even it doesn’t mean you gotta choose based on that it means maybe I’m just able to close a few doors and say, I know that’s not for me, and that allows me to focus and double down in the areas that I’m drawn to. But that’s something I think I could have taken advantage of more, and I certainly encourage students looking today to use that secret weapon.

0:19:21.0Sign off

Karina: Well, that is fantastic advice because... Yeah, I don’t think a lot of people realize that just reaching out, shooting your shot... And I think I agree with what you said in college, a lot of people will reach back out to you. So thank you so much for this great interview today, it was really just fascinating getting to talk you and... Yeah, I think you gave some great advice, so thank you so much.

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