Faces of data science 1. Interviews with Siddharth Kumar, Lisa…
By: Casey Doyle
Tags: Data Science Professional, Data Science Experience, Azure, Data Science, Data Science Education

Siddharth Kumar | Lisa Cohen | Scott McBride

Until recently, few people had “data science” in their titles. Today, there are thousands — many thousands — which raises a few questions: Who are they? Where are they from? And what, exactly, is data science?

While data science is a relatively new discipline, the work done by data scientists is not. Gil Price published “A Very Short History of Data Science” in Forbes, and Stephanie Dick published a similar article on “Artificial Intelligence” in the recently launched Harvard Data Science Review. Both articles point out that our understanding of “data science” and “artificial intelligence” evolved from ideas that began in the late 1950s and early 1960s.

In fact, a lot of data science depends upon statistical techniques that were developed well in advance of computers: Karl Pearson, William Sealy Gosset (Student), Ronald Fisher in the early 1900s, Francis Galton and Pierre-Simon Laplace in the 1800s, and Thomas Bayes in the 1700s.

So what is data science? The best — and most durable — definition that I’ve come across is by Jeannette Wing, director of the Data Science Institute at Columbia University: “Data science is the study of extracting value from data.”

Until recently, however, there were few — if any — university degrees in data science. So Casey Doyle, a Senior Program Manager in my org and co-editor of Data Science at Microsoft, spent time with a few of the managers on my team, Siddharth Kumar, Lisa Cohen, and Scott McBride, and asked them how they ended up as data scientists. This is the first in a series of interviews that we call “Faces of Data Science.”

— Ron Sielinski

Siddharth Kumar

What’s your educational background, Sid? I have a Master of Science in Analytics from Northwestern University — I was among the first graduates of that program when it started — and prior to that, I completed my bachelor’s degree in electronics and communication engineering.

As someone with a degree in data science, did you find the master’s program helpful? It covered all the fundamentals, which I was pretty familiar with already from my work. One thing I really liked about the program is that they took a structured approach that allowed me to fill any gaps I had in fundamental concepts. I was expecting more on machine learning and deep learning, which was lighter than my expectations. My bachelor’s degree also focused on computer science, which helped me start my career as a software engineer, and it paid a lot of dividends later for me to become successful in machine learning and deep learning.

What led you from being a software engineer to becoming a data scientist? It’s been an interesting journey. My first job out of school was developing cutting edge software in the automotive industry. I was writing code to make next gen car engines smarter and intelligent, and then an analytics opportunity came up and I took it because I’d always had passion around mathematics since my earlier days at school. The analytics work, combined with my love of math, interest in coding, and attraction to business intelligence as way to solve problems led to data science and it was transformational for me. I have since then gravitated more and more toward machine learning and deep learning.

What do you like most about your work as a data scientist? I can’t point to only one so I’ll mention three things. First is the opportunity to learn new things in this space which I am passionate about, since even after ten years in this field there are still new things for me to learn every day. The second is the challenging nature of the work itself, especially because it’s an applied science with a variety of business problems that are very fuzzy — you have to connect the dots and detect patterns from disjoint or ill-connected systems. And the problems don’t repeat so they’re always new. The third thing I like is the creative aspect. Creativity applies at each stage of the process. When you define the problem, you have to be creative. When you’re thinking about how to solve it, you have to be creative, because you can often take a number of data science approaches to the problem. Do you solve it as a complex problem, or break it down into smaller problems and solve each of them? How do you measure success? Choosing the right answer must make sense not only from a data science perspective, but also from a business perspective.

What has surprised you the most about your work as a data scientist? Earlier in my career, one thing that really fascinated me is that you can predict things in advance. Looking back to early days, the models we were able to make in those days now seem trivial compared to the problem space we are focusing on now. Today, the amount of research in machine learning and deep learning had led to numerous applications of AI in our day to day life; the progress has really surprised me. Today when you stream shows you get recommendations, or when you’re shopping on Amazon.com, or even when you’re driving your car on autopilot. The way AI is getting deeply embedded into our lives amazes me as a user, and as a data scientist I can see the other part of that world as well. When Amazon.com shows me an ad, I always wonder, what kind of data are they collecting, what kind of algorithm are they using, and if I do or don’t click on an ad, what will happen? So it’s interesting from the perspective of not only consuming the technology but also thinking about the back end and how it was built.

Given the rapid change and development you describe, how do you continue to learn? I think working in machine learning and data science means you have to keep learning on a constant basis or you will quickly become stale. You have to keep yourself up to date on the major advances and breakthroughs in technology, and I’m fortunate in that I’m a member of a team in which continuous learning is deeply seeded into our culture.

There are three channels on which I particularly rely, the first being the conventional channel of books. I still refer to The Elements of Statistical Learning, which I first encountered in grad school, and I keep going back to it to refresh many fundamental concepts. I rely on many other books as well. The second is the digital channel which includes keeping up to date with blogs like fast.ai and KDnuggets and research blogs/publications from companies like Microsoft, Uber, and Netflix, and apps on my phone like Feedly, which summarizes the different blogs I follow to keep me up to date. Social media is another one, like Twitter and LinkedIn. Influencers I follow there include DJ Patil, Yann LeCun, and Jeremy Howard, among others, who are the visionaries or are at the cutting edge of AI. Finally, I learn a lot from the data science community including the talented and diverse set of data scientists on my team. They come from different backgrounds and bring lot of unique, complementary, and innovative ideas and perspectives. Also, the rich data science community I can reach out to at Microsoft!

How would you advise someone who wants to get into data science today? There are so many different resources and courses out there, but I recommend taking a more focused approach. Find out the two or three things you want to focus on initially. For example, with programming languages, don’t start with both Python and R, instead choose one of them and then get really good at it before going on to the next one. To pull data, which happens before you can apply an algorithm or build a complex neural net, get really good at SQL or any other data processing tool like that. I believe in learning by doing and hackathons and Kaggle are great places to start. Take a problem and try to solve it end to end. Read the discussion forums, and then read the solutions of previous winners. To become successful in the field of ML, you must code, code, code. Don’t run away from writing code, because as someone has said, machine learning is founded in mathematics, expressed in code, and assembled into software. Also make sure you partner with non-technical people such as business stakeholders. Their perspectives will always help you. I also recommend making a portfolio of your work on GitHub so you can show your work with something creative you’ve done and stand out among the other data scientists out there.

Lisa Cohen

What’s your educational background, Lisa? I’ve always loved math and science. For math, it’s the overall structure and the joy of solving a puzzle; and for science, it’s the way that it helps us understand the world in a rational and logical way. My degrees are Bachelors and Masters in Applied Math from Harvard University. I also took courses at Harvard Business School and MIT Electrical Engineering programs during this time. I really liked academia, but ultimately decided to pursue a career in industry. I felt that at Microsoft I could work on interesting problems while also shipping to an end-user, with an ongoing ship cycle, and gain user feedback along the way.

What led you to become a data scientist? When I first joined Microsoft, I worked in a software engineering org developing Visual Studio. I was curious how large corporations like Microsoft run the business, and excited to have an impact on millions of users. In this role, I was analyzing customer feedback and usage patterns, and designing our telemetry systems. Then as the field of data and analytics grew, I started working on both product and business analytics. After working in this capacity for Visual Studio, I joined the Customer Growth Analytics team to help lead data science for Microsoft Azure.

What do you like most about your work as a data scientist? I feel really grateful to have discovered this profession, since I feel it’s a great match for my passion and interests. I enjoy the whole problem solving process, starting from a broad goal or question, developing a mathematical model or metric to represent the scenario, and then using the analysis to drive business impact. We bring together data scientists with a variety of backgrounds, and it’s fun to come together to research approaches and compare best practices. As the role of data science has grown, it’s exciting to see this work being used across the company to inform data-driven decisions.

How do you continue to learn? The field of technology is constantly evolving, so it’s important to be constantly learning. Microsoft CEO Satya Nadella has established a “learn-it-all” culture for the company, which supports this as well. There are many great resources available in the field of data science. Our team has a set of licenses to datacamp.com, which is a nice place to practice additional concepts. I like to read and lead a book club for our group, where we can discuss new approaches together, from books and articles. Conferences are another source of information sharing for industry best practices and techniques. Within Microsoft, there are also great opportunities to learn from mentors, by sharing ideas across data science teams, and partnering with Microsoft Research.

How would you advise someone to get started in data science? One approach I often recommend to people looking to make a career change is to find a project you can do in your current role to practice these skills. This is a great way to see how much you like it, figure out what you need to learn, and gain experience. Learning in the context of a deliverable can also help provide a deeper understanding. Let your current manager know about your interest, and oftentimes he or she will be able to support you in this. This can work particularly well if you use this opportunity to identify new approaches that accrue toward the overall business charter or goals.

Scott McBride

What’s your educational background, Scott? I went to UCLA for undergraduate, majoring in political science. I was also a member of ROTC, and so unlike most other political science majors, I had a heavy dose of higher level math and stats and physics as part of requirements for the Navy. After the Navy I went to Harvard Business School (HBS) for an MBA.

Did you pursue any particular course of study during your MBA? At HBS there are no specific specializations, the MBA is a general management degree, but you can customize your curriculum for the second year. So I took as many technology-based classes as I could as well as some financial modeling and operations classes because I wanted the hands-on analysis skills they provided.

What led you from there to working in data science? At HBS we studied a case on Microsoft and they typically bring in one of the real-life protagonists to talk to us and in this case we were fortunate to have Satya Nadella. That was around 2012, when he was running the Server & Tools business, before he became CEO, and having a small group discussion with him was pretty cool. By then I had already done my internship with Microsoft and it reinforced for me that the company has great leadership in data in the cloud, so I joined the company after graduation.

Did you come to Microsoft to specifically work in data science, or did you choose data science after starting? I would say more of the latter. I was fortunate to get into a rotational program during my first year, during which I spent some time in SQL product marketing and then Azure business intelligence, and I fell in love with the team. The culture there and digging into the data was great, and the chance to get a seat at the table to improve the business by knowing and applying the data. I was supposed to rotate to a third group but, to quote Wayne Gretzky, you don’t skate to where the puck is, you skate to where it’s going to be, and given all the interest in being data-driven and in the cloud I thought this would be a pretty good place to stay with lots of opportunity and growth in the business.

What do you like most about your work in data science? I like that you get to see the breadth of different businesses and it’s your job to bring things together and make sense of them. You have to be very thoughtful about where you spend your time, because you can get lost in the data otherwise. So on the one hand, there’s time pressure, but on the other, it’s all somewhat unbounded in terms of driving insight. Being the subject matter expert is very empowering, particularly when you land something and changes are made as a result.

What has surprised you the most about working in data science? I’d say the impact we can have. It’s always a pleasure when we can use our data knowledge to up-level the conversation to implementing change or driving improvement. In that way we’re not a research organization, we’re trying to drive value to the business.

What is important, then, for data scientists to learn on an ongoing basis? Three things come immediately to mind. First, taking advantage of the latest techniques or technologies, such as the latest in R or Python or Kusto, is very important. Second, understanding the specific data domain being worked on. We can have the best technique in the world, but if we’re not taking the time to learn our data sets and understand what good looks like and how to identify a problem with the data and put it into context, it’s not enough. We need to be subject matter experts of the data. Third, it’s important to know the particular industry or business domain we’re working in, and how customers are using it so we can drive positive outcomes for them. It’s very challenging to do all three and balance against near-term deliverables but it’s essential.

How would you advise someone who wants to get into data science today? There are a lot of online training and other courses people can take that teach technical skill sets and analytical techniques. But I think that what people sometimes miss when I’m talking to them about an open role is how to apply their knowledge of data science to their day-to-day job, even if they’re working in a role outside the profession. I think it’s important to apply what you’re learning to the real world whenever you can.

You can read more about Scott in a People of Microsoft profile here: https://news.microsoft.com/life/the-skys-the-limit-working-in-data-and-the-cloud/