Faces of data science 2
By: Casey Doyle
Tags: Data Science Education, Data Science, Azure, Data Science Experience, Data Science Professional

For the second in our “Faces of Data Science” series, I’ve interviewed three colleagues from varying backgrounds: Vlad Rișcuția, data engineering; Selena Zhong, data science management; and Sven-Erik Nielsen, program management.

Vlad Rișcuția

Principal Architect, data engineering

What’s your educational background, Vlad? I have a Bachelor of Computer Science from West University of Timișoara, in Romania.

What led you to become a data engineer? I came to this team from Office where I was a software architect on the Office growth team, focusing on developing in-product features and growing the business. I was passionate about the growth space, including questions about how to measure a business, growth and adoption, that kind of thing. When I was looking for a change, I came to the growth team in Azure, where I became a data engineer, focusing on building solutions with Azure. Our engineering team here works on data movement and the data platform.

What do you like most about your work as a data engineer? It’s been a lot of fun. When I was in Office I was working mostly on the client side — in Office for Android, Office for Windows Phone, and Office for Windows — and that was mostly native C++ client code. Moving to building Azure solutions with big data and building our data platform has been a very fun learning experience, and I like all of it. Even though the technology is quite different, software architecture — designing systems, talking to stakeholders, setting direction and so on — is ultimately the same.

You’re also a published author. Can you talk about your book? The book is called Programming with Types, published last October, and it’s about using the type system of your language of choice to write better and safer code. There’s a story from real life to illustrate what the book is about: You might remember the Mars Climate Orbiter spacecraft that crashed when it arrived at Mars in 1999 because of a software issue. NASA and Lockheed developed two components separately, and one of them was measuring momentum in metric units while the other used imperial units, and when they put them together it caused the spacecraft to crash during orbital insertion. It showed that if you’re saying momentum is a number and you pass that number from one function to another you can hit this issue. If, instead, you declare two separate types for those units of measure, your program in the compiler won’t let you pass one to the other. Most programming languages, but not all of them, have some kind of type system, and the reason is to allow you to express these kinds of constraints with componentized and safer code. That’s what the book is about.

That’s very interesting. How would you say that you continue to learn? There are a bunch of ways. There’s learning on the job — we’re always picking up some new Azure offering and looking to understand how it works. I also use Pluralsight a lot, which is a great resource. I read books on software engineering and software architecture. I try to go to conferences, such as the Strata conference, which is devoted to data science and data engineering. At conferences it’s awesome to see what folks across the industry are looking at and realizing they are facing the same challenges and trying to solve some of the same problems we’re trying to solve. Also I look to the Internet, of course, blogs and so on, for more learning.

How would you advise someone who wants to get started as a data engineer or as a developer in general? I would say jump into it, especially like I did for data engineering. For anyone who’s passionate about it, I would say start coding and start learning.

Selena Zhong

Principal Data Science Manager

What’s your educational background, Selena? I have a Ph.D. in Statistics and a Master’s in Mathematics from Arizona State University, and before that, a Bachelor’s in Mathematics from Fudan University when I was in China. I’ve always been very passionate about mathematics and statistics, and so I decided on the latter for my research focus area in nonparametric data analysis. My dissertation was about curve registration using Bayesian methodologies. I’m still very passionate about that topic.

What led you to work in data science? It was a natural step because of my educational background; I’ve always been very passionate about mathematics and data science. Even during my personal time I would try to solve mathematical puzzles. When I graduated there were not yet the words “data science” as we know them today. I’m very excited to see that people who are passionate about data and using mathematics to solve problems have formed the data science discipline.

After your Ph.D., did you work in academia or come directly into industry? After graduation, I worked at Amazon, as part of their Prime program. We were called business intelligence engineers, and our mission was to use data to help the business discover insights from customers to uncover business opportunities. In 2009 I came to Microsoft and worked in a variety of roles, including analysis and decision science before the data science disciplined was formed.

What do you like most about your work in data science? Uncovering insights from data, especially big data. The data forms into something of a puzzle, and you solve it to find out what it’s telling you. On top of that, working in industry allows you to translate those insights and findings into business strategies and business decisions that make an impact, and at Microsoft, that tends to be at a big scale. Seeing the development of business opportunities is really fun for me.

What has surprised you the most about your work in data science? The evolution of the data science discipline. When I started in tech, there were program managers and software engineers doing some data analysis but not a well-defined discipline for people who are passionate about using data and uncovering insights from it for business impact. Since then I’ve seen data science organizations created, grow, and formalize across multiple product groups in the company and it’s been a fascinating and exciting thing to see the evolution of it all.

How do you continue to learn? Working in the tech industry, you always need to keep yourself current with the latest technology. The way I do it is in two parts. One is technology on the data science side — the statistical side. I read journals from statistical associations for articles relevant to what we do, which can lead to interesting, cutting-edge solutions to some of the challenging problems we face. Another is keeping myself current with the business component — the domain side. I work in commerce in the payments domain and it’s fast evolving, and so I attend conferences related to the industry. I try to combine the two to help make a bigger impact with my work.

How would you advise someone who wanted to get started in data science? Because of my background, I think being very good at mathematics is a big help. A solid foundation in mathematics sets the stage for everything in data science, from statistical methodology to model algorithms to even how to form a business case. Someone who’s thinking about data science somewhere along the line in their career would find a solid mathematical foundation very helpful.

Sven-Erik Nielsen

Senior Program Manager

What’s your educational background, Sven-Erik? I completed my undergrad in international studies at the University of Washington focused on international political economy. I earned my Master of Science in Predictive Analytics from Northwestern University in Chicago and I’ve also completed a professional certificate in decision analysis at Stanford University.

What led you to work in data science? My career started out in policy analysis — looking at large systems and their interactions to understand the impact of policies. Clients often asked, “What is going to happen if X or Y event occurs?” The more this type of question arose, the more I began to explore ways of answering it with a more rigorous set of tools than those associated with conventional policy analysis. I later joined Starbucks, working across their R&D and supply chain spaces. I had an opportunity to work on a side project with what was then the precursor to the current loyalty analytics team, writing rudimentary R scripts and seeing the application of analytics in action. This validated my interest in pursuing analytics as a path and ultimately led to me completing my master’s degree in predictive analytics. Long story short, thanks to an incredible sponsor, I was given the opportunity to start my own analytics team. We focused on identifying customer experience and quality supply problems and suggesting possible solutions across Starbucks 27,000 stores in 75 countries. And now, I’m working at Microsoft on Azure!

How would you describe your approach to program management? My focus in analytics program management has been about pulling together different capabilities based on the business problem to be solved: The capabilities to apply an analytical lens to the problem, engineering to pull the data together, and so on. I’m continuing that chain here at Microsoft as a program manager, working in a “connective tissue” role where there’s a combination of the engineering aspect, the analytical aspect, and the business stakeholder aspect, which all need to be orchestrated.

What do you like most about your work in data science? First is the inquisitive nature of the work — if you’re an intrinsically curious person, there’s no better practice out there. Second is the challenge: Data science is hard. It requires rigor and craft. From the context of analytics program management, even if you’re not doing the analysis directly, you still have to be aware of the methods, their limitations, appropriate vs. inappropriate applications, failure modes, and so on.

What has surprised you most about your work in data science? The first is how nontrivial and foundational it is to correctly define the problem to be solved. It doesn’t matter how sophisticated the analytical method you apply if you’re solving the wrong problem — you’ve still solved the wrong problem. (If you’re interested in this space, I recommend the work around decision quality pioneered by Ron Howard and Howard Raiffa.) The second surprise is managing the expectations surrounding data science. While it’s a bit of hyperbole, many stakeholders expect data science to “pull a rabbit out of a hat.” Data science is fundamentally an iterative process and the road to success is littered with a lot of false starts and dead ends. Third — but not least — is how crucial it is to empathetically step into your stakeholder’s world and walk with them. Their domain expertise and experience is probably your most important data set, and their willingness to act on your insights or take a dependency on your model is the ultimate arbiter of your team’s success.

How do you continue to learn? In the Customer Growth Analytics team I’m part of, we’re lucky to have a culture that values continuous learning: Our org dedicates a chunk of time for professional development every week. For me personally, I believe that a program manager needs to hurdle a minimum bar of credibility in the end-to-end process of data science, so continuous learning is paramount. A few resources I regularly leverage are Microsoft Learn, Pluralsight, and DataCamp. Meetups are great, too. I’ve found learning by doing to be the best practice in helping me understand concepts and tools at a deeper level. While it’s not always possible, I try to take opportunities to peel off some small technical parts of a project to work on alongside data scientist teammates. Hackathons are also great because they provide a compressed applied approach in a single chunk of time.

How would you advise someone who wanted to get started in data science? First, practice the art of asking questions. Your analysis is fundamentally informed by the problem you’re trying to solve and often it takes a number of questions, iterations, or sessions before you can identify the difference between the superficial problem and the real problem. Questions are a tool, so it’s important to be intentional and thoughtful in learning how to use them: Sometimes you need divergent opening questions to expand and explore the problem space, and other times you need to apply convergent closing questions to zoom in on a specific area or to drive closure. My second recommendation would be to realize that data science is not a dark art — it’s not magic or inaccessible. It comes down to thoughtful application of a common set of methods, starting with a solid grounding in statistics. Try solving the problem with the simplest approach possible and iterate — I often think through the lens of achieving lift relative to the current baseline rather than lift relative to the theoretical maximum. Third, raise your hand and try to solve a small problem. Find something that interests you and ask to join those working on it. It’s fine to say, “I don’t know how this works, but I would like to help.” I’ve found that people are almost always happy to help when you show genuine interest and a willingness to learn.

With all your varying experience, where do you see data science headed? There’s an interesting dynamic at work: The volume of data is expanding and the tools to work with it are becoming ever more sophisticated, making traditionally difficult tasks much easier. You see manifestations of this in the phenomena of tech intensity and the citizen developer. As a result, more and more people are seeking to solve questions with data. This also presents the risk of applying these tools in inappropriate ways. I think there is a growing role for what’s been variously called analytics translators, data translators, or data product managers. These roles can communicate fluidly between business stakeholders, data scientists, data engineers, and privacy experts to deliver solutions in an agile and iterative manner. For the data scientist and engineer, you need to bring clarity, noise filtration, and the ability to unblock. For the business stakeholder, you need to bring empathy, curiosity, and the ability to deliver solutions they can use. (Spoiler: they don’t care about the model, they care about what it can do for them.) In terms of other forward-looking topics around data science, I’m very interested in how discussions around the societal impacts of data science and machine learning will change the way we use these tools and methods. Two individuals at Microsoft doing interesting work in this space are Glen Weyl and Brent Hecht — I’m very curious to see how this conversation develops.