Interview: Ali Vanderveld, Groupon on How Data Science is Changing the Global E-commerce Marketplace

We discuss the tools used for data science, competitive landscape, journey from astrophysics to data science, advice, skills sought in data scientists, and more.

ali-vanderveldAli Vanderveld is an astrophysicist turned data scientist. After receiving her PhD from Cornell University she worked as a postdoctoral scholar at Caltech and the NASA Jet Propulsion Laboratory, and then as a research fellow at the University of Chicago. During this time she was a member of the development teams for several space telescope missions, including the SuperNova Acceleration Probe, the High Altitude Lensing Observatory, and Euclid. Ali joined Groupon in 2013 and she currently works on the Data Science team, where she uses historical data and machine learning techniques to study issues pertaining to sales and marketing.

First part of interview

Here is second part of my interview with her:

Anmol Rajpurohit: Q8. What software and technologies did you use for forecasting demand and prioritizing merchant acquisition? technologies

Ali Vanderveld: On the data science side we have a Teradata database and a Hadoop cluster, and we most commonly use R and Python for modeling. The internal tools that bring it all together are in a combination of Ruby and Clojure. The front end is an Ember application.

AR: Q9. Since more than half of Groupon’s business comes from mobile devices, from an Analytics perspective, what major differences did you observe in customer behavior over mobile platform (smartphones, tablets, etc.) compared to web platform (laptop, desktop, etc.)? groupon-mobile

AV: Mobile customers tend to be more engaged than our web-only customers, purchasing more frequently and spending more. Though mobile purchasers are more engaged, it takes longer for a mobile customer to activate.

AR: Q10. How do you distinguish Groupon from other competitors such as LivingSocial and Amazon Local?

AV: No one else has the scale and breadth of deals as Groupon. We’ve evolved from a deal-of-the-day site into a global ecommerce marketplace with livingsocialmore than 425,000 active deals. This progression has reshaped our business into one with a majority of transactions occurring through mobile devices with a focus on three key categories: Local - health, beauty and wellness, things to do and food and beverage; Goods - electronics, home furnishings and apparel and Travel - hotels, resorts and tours. We also see a number of interesting opportunities to more closely align and leverage these categories. For example, if someone books a trip to Miami, we have an opportunity to offer them inventory from Goods to help outfit them for the beach as well as restaurant deals and things to do during their trip.

AR: Q11. What motivated you to be an astrophysicist and later, a data scientist? Do you see any similarities between your past work in astrophysics and current work at Groupon?

AV: When I started college I actually wanted to major in music performance but I eventually decided to switch to something that I thought astrophysicswas more practical, physics. Little did I know the long and difficult career path awaiting me in my new choice! Eventually, I grew weary of working in such an impersonal field and decided to pivot to using my scientific know-how on human behavior. Now I study how people interact with the ecommerce world. Despite the drastically different subject matter, there are definite parallels between the day-to-day life of working in theoretical astrophysics vs data science. I still spend much of my day writing code and relying on my background in statistics. I no longer need advanced differential geometry, but now I get to learn data mining and machine learning.

AR: Q12. What is the best advice you have gotten in your career? adaptability

AV: It’s advice I’ve gotten on multiple occasions -- to always be flexible and adaptable. It’s advice I’ve put into practice in my career path changes from music to physics to data science. It’s also important advice within the data science field itself; everything has to be scalable and the only way to keep up is to constantly experiment with the latest technologies.

AR: Q13. What key qualities do you look for when interviewing for Data Science related positions on your team?

python-rAV: We have a team full of brilliant people from a wide variety of backgrounds, ranging from business to math to computer science. Above all else, we’re looking for smart and creative people. Experience with the specific tools we use (e.g. R and Python) is nice to have, but this is easy enough to pick up on the job if the candidate has a solid technical background.

AR: Q14. On a personal note, are there any good books that you’re reading lately, and would like to recommend?

AV: I read it a few years ago when it first came out, but I plan to re-read it again -- “Bossypants” by Tina Fey, one of my personal heroes.

anmol-rajpurohitAnmol Rajpurohit is a software development intern at Salesforce. He is a MDP Fellow and graduate mentor at UCI-Calit2. He has presented his research work at various conferences including IEEE Big Data 2013. He is currently a graduate student (MS, Computer Science) at UC, Irvine.