Yogi Saxena is not one to back down from a challenge. The distance runner ran in his first marathon just two years ago in order to win a bet. Next month, he competes in another grueling marathon, his third. And if that were not enough, a friend’s Facebook post inspired him to train for a sprint triathalon. “I taught myself to swim when I was young,” Saxena says, revealing that his drive to learn new skills started early. “And if it wasn’t for the swim part, I’d have done an Olympic-distance triathlon instead.”
Saxena’s love of mastering new challenges is likely responsible for his decision to pursue data science as a second profession, after having a successful career as an electrical engineer. Currently at Boeing, he is responsible for developing a tool that would help visualize feeds from various classified and non-classified sources.
He is profiled here as part of the Strata community profiles.
How does a successful Telecom engineer end up with a career in data science?
Yogi Saxena: I started as a Test Engineer at a small start-up wireless communication company. I was using tools to understand how the system was behaving, so I was able to get a good understanding of both the hardware and the software.
I started working in security after 9/11. The emphasis was on securing the infrastructure, and wireless was one of the major players. They were very anxious to make sure their systems were secure. While I was working, I was fortunate enough to be going to school and taking classes here and there.
You took your introduction to data science classes at Columbia. Tell me about the experience of going back to school to learn the fundamentals of data science.
Yogi Saxena: The students were mostly from a statistics background. In most of my work that I had been doing professionally, we were not using those kinds of tools. The work we were doing was quite manual — using our facts and logs to arrive at some kind of probability on how much we can derive from a particular data set.
In the introduction to data science class, I was able to see that there are several new up-and-coming tools and companies that leverage the principles of math, science and statistics to make it a little easier to get to the heart of the problem. I was very happy to see a different side, and at the same time quite a few things that were common to what I was doing professionally.
In one of the lectures in class there was a reference to the New York Times project called Cascade. Without going into too much detail of the project itself, the goal was to track how the web link or URL of a news article propagates across various media sharing sites such as Twitter. For visualization they used a tool called Processing. I’d never heard of it. This tool helped to plot a three-dimensional graph which was visually aesthetic and informative at the same time. It made it easier for a casual observer to answer simple questions as to which social media sites are most popular, what time of day a news article is drawing positive sentiment, and at what times users are the most active.
And can you give an example of how returning to school added value to your professional practice?
Yogi Saxena: Visualization, for example, was not very big in my professional experience, but here were several tools I wasn’t aware that you could use to create all these fancy charts and graphs to make the problem more understandable — not just to yourself, but also to a larger audience who probably are not looking at it from a deeper angle.
For me, it was a new way to look at data. For work we were just using small graphs or Excel or Power Point at the most, and here we had all these fancy tools that were available to us. It was a completely new field, not just someone looking at the data at the bits and bytes level. Somebody can take data and transform it into a completely awesome, awe-inspiring picture. It was just mind-blowing to me.
Looking forward, are there any potential innovations in data science that you are excited about?
Yogi Saxena: I see data science as a tool that you can pass on to the consumer. I see the wireless boom and the smart phone usage continuing, and I see companies using the data they have from their customers and actually providing it back to the users.
For example, for a wireless service provider, an upgrade to their networks could be disruptive and result in customer dissatisfaction, loss of revenue and bad PR. By using data science, a service provider could proactively issue some form of credits to affected customers and help mitigate such loss of revenue.
Your bank could be able to give you a better idea of where your money’s going – where you’re spending your money — analyze the data, give you options on what you could do to improve it. So I see data science companies providing tools to users to be able to make better decisions.
This interview was edited and condensed.