Every Tuesday in April: Free Intro to Python Training Series for Business Professionals Register Now
Speaker Series: Siddharth Motwani, Sr. Data Strategy Analyst at Priceline.com
By Emily Wilson • March 23, 2016
In the latest installment of our Speaker Series, we talk with Siddharth Motwani, Sr. Data Strategy Analyst at Priceline.com, before he gave his in-class talk to the Data Science Bootcamp cohort in NYC. Metis Sr. Data Scientist Vinny Senguttuvan asked about Motwani's role at Priceline, the company's data-related goals, and more. Watch the interview below and read the full Q&A, too.
Vinny: Welcome to Metis, and thanks for taking the time to talk to our students. To start with, can you tell me a little bit about what you do at Priceline and what your interests are?
Siddharth: I work on the product strategy end for Priceline customer conversions, so essentially, we own the main supply and product-driven breakouts between hotel, rental car, and air. My team directly drives the insights and strategy using data, analytics, science, whatever it may be.
Vinny: So you would call the model data-driven at Priceline?
Siddharth: Oh, absolutely. Everything from searches, volume traffic bookings, contributions, which is how much we make per booking. Everything. We look at everything under the sun.
Vinny: That's cool. I know some companies start as data companies and some do it haphazardly and at some point they understand the importance of it.
Siddharth: Priceline has been around a long time. I think the data movement is still fairly young, which is weird to say because it's always been there. But given the resources that we have now and what we can actually do with it, it's completely changed. Many think that Priceline may have been late getting to the game, so it almost seems like we are doing catch-up, but we've been making incredible strides. Even in the seven, eight months that I've been there, things that I've seen overhauled, t's just incredible. It's really exciting to be a part of, to be honest with you.
Vinny: That's great, because like you said data science is a fairly young field. For the first few years, like the previous decade, it seemed like it was all focused on a few major corporations using it to further business. But now with more data available, it's branching out into a lot of different sectors, like nonprofit and journalism. We see that in our student body. We have students who are coming from different areas and they are trying to do more things that are not bound by the company or profitability. I think that's exciting.
Do you know of any other people who are doing a good job of that?
Siddharth: I shadowed up at Zenefits. I don't know if you're familiar with them. They do a great job of focusing on people who are outside of their niche and bringing them in and using their expertise in whatever that may be. Uber as well. Those are two good ones.
Vinny: As far as trends you see, is there anything students should be looking at more in terms of technology or skill set?
Siddharth: A couple of things. I think scraping is huge. Also, at the end of the day, understanding your market and understanding what your competitors are doing is extremely vital. This is what I learned from the Metis hire we had. Scraping was a big thing for him. It was really cool learning from him, and it was very alien to me. I would say that's proved extremely beneficial.
The other thing I would say is focusing on big data. It may be cliche, it may not be, but I think being able to take any data set - and usually big data is not very clean - and actually turn it into value...understanding how to query, how to turn that into insights, and turn that into predictive models is a challenge. I think there is lineage there, but not as direct as you might think.
In this post, SwitchUp interviews Brendan Herger, a Metis Sr. Data Scientist based in San Francisco. Find out what he loves about teaching, about his passion for machine learning, and how he helped found Capital One's Center for Machine Learning.
Paul Trowbridge, instructor of our upcoming Live Online Statistical Foundations for Data Science & Machine Learning course, discussed the need for a firm stats foundation, talked about his career, and more during a recent Q&A.
Going through a data science bootcamp is an intense experience for everyone involved. Students work at a breakneck pace unparalleled in other learning environments, absorbing new and difficult concepts and skills, and applying them to projects starting as early as week one. All the while, instructors shoulder the hopes and fears of their cohorts as they guide and teach them over the course of 12 weeks. For interested individuals, teaching a bootcamp can have tangible career benefits related to industry goals and aspirations. Read how here.