What does the career path and a day in the life look like for the Director of Data Science at iRobot?
We interviewed Angela Bassa to find out.
Where did you grow up? What did you parents do for work? What was your very first job?
I was born and raised in Brazil and moved State-side when I was 14 with my parents when my dad transferred to WV for work. My father is an electrical engineer and my mother is a pedagogue - I definitely got a lot of my passion for science and learning from them.
When we first moved to the US, my mother and I weren’t allowed to work based on our visa status. My father has since worked in Mexico, France, and Korea; when I got accepted into MIT he got transferred again, so I’ve been living in a different continent from them ever since I was 17.
After graduating from high school I came to the Boston area for college and have been living here on-and-off for almost 20 years now. My first jobs were college internships where I tried to get a sense of the different kinds of work opportunities that existed for a mathematician. My first one was in the venture arm of a large multinational bank, then I spent a summer in the comptrollership of an aluminum smelting plant. I also spent a semester at the MIT Research Lab of Electronics in the Quantum Information Group where I worked on a project investigating the communication protocols necessary for getting two quantum computers to transfer data packets between each other. For my senior year, I conducted a comparative study of presidential election media coverage, highlighting the disparity between the alleged objectivity and impartiality of newspapers, and their potential subliminal impact as political agents during election campaigns. This “testing of the waters” was invaluable in helping me figure out what to do after graduation.
Why did you decide to study Mathematics at MIT? Anything that you’d like to highlight from your time at MIT?
I mean, it’s MIT! To be frank, I never thought in a million years that I’d get in. I also didn’t apply to any “safety schools” because I figured that if I didn’t get into any of the ones I did apply to, I’d take some time to prepare for the Brazilian university system’s entrance exams and attend school there. But then the fat MIT envelope arrived (I’m old enough that they still let you know via envelopes back then) and I knew that I had to go there—it had been a dream of mine for a long time. Studying at MIT was amazing, but it was probably one of the hardest things I’ve ever done; those classes are no joke, and there’s no room for slacking. It took me years to figure out just how uncommon the pace and rigor of MIT classes were.
I remember that I wanted to study computer science or economics when I first got there. There are some classes which all MIT students, whether they major in biology or music, must take—and they are pretty math-heavy. I had taken several advanced math classes in high school and fell in love with the subject even more at the Institute… I remember being conflicted about it, thinking “What am I going to do with a math degree when I graduate?!” But it turned out that the timing was perfect since the data analytic revolution was just around the corner.
What was your first job out of undergrad?
My first job was as a financial analyst on Wall Street. The finance sector recruits heavily for quants graduating from mathematics degree programs, and I couldn’t really believe that I was going to be working in Manhattan… but it turned out to have been a poor fit for my personality. As with any first job, you’re really paying your dues—so if you don’t enjoy it, the long hours and subject matter can get to you quickly. I worked in the Secured Capital Markets division, which meant that I had to be at the bank when the trading floor got in at 6:30 AM and didn’t leave until the investment bankers wrapped up around 9 PM or 10 PM. I learned a ton, and I’m glad to have had the opportunity to learn about capital structuring, complex deal financings, contract negotiations, etc., but it really wasn’t the kind of career I could see myself investing in for the long term.
I’m jealous… it looks like you spent a year living in the Virgin Islands as a SCUBA instructor, which must have been amazing. Was that a way to take some time to think about your long-term career goals? Did you find that experience beneficial?
This closely follows from the previous question: after a year of 100-hour weeks, I decided I needed to “decompress” (pun intended!). I packed up my nest egg and moved to the beautiful Caribbean island of St. Croix where I sold T-shirts for minimum wage while I racked up logged dives to qualify for the open water certifications. After about six months, I had jumped through all the required hoops and became a SCUBA Instructor. I then spent another six months or so as a teacher and dive master. This was definitely meant to be a time for me to recalibrate and figure out what I wanted to do once I rejoined the real world, but I honestly learned more about managing people under stress during this time than at any other “management” course I’ve taken since. Once you’ve had to safely deal with panicked divers who want to bolt to the surface (a very big no-no when you’re breathing pressurized air) or handle an inexperienced mother-daughter pair who could get mightily distressed if they saw that harmless but fierce-looking barracuda right behind them, many other stressful situations in the corporate world become a lot more manageable in comparison.
Prior to joining iRobot, you worked in various roles that were focused on working with very large data sets and analytics within industries like healthcare and energy. How did that experience help you build the foundation for what you are doing today?
Working in such different industries has been very helpful because it has allowed me to become versed in different techniques that I can now use in a cross-disciplinary way. That “cross-pollination” has been a great source of ideas and inspiration for novel approaches to solving data problems. For instance: the work I’ve done analyzing and planning clinical trials has helped me think through A/B tests in software environments, simulating genetic trait introgression in soybeans has been a huge source of inspiration in designing energy efficiency models for building management systems, and so on. I think this has also given me a new perspective in hiring and management: one of the data scientists on our team has a marine biology background, and she often has surprising (to me) insights about robot fleet behavior that she derives from her time researching dolphin pod migration. Knowledge really is all around us and keeping an open mind to identify where seemingly disparate applications could transform an approach is incredibly useful.
Can you share the high-level responsibilities of your current position at iRobot and what your Data Science team is working on?
Our core responsibility is to study the behavior of our robot fleet in order to delight our customers and increase our revenue. This work is a combination of pure research as well as developing the tools and infrastructure that allow these scientific inquiries to be assessed quickly and accurately. The questions we ask run the gamut from "What is the reliability of our data collection platform?” to “When do our customers prefer to run their robots and why?”
Day in the Life
Coffee, tea, or nothing?
My drink of choice is a large iced coffee from Dunkin Donuts—even if it’s 20 degrees outside! I’m definitely a Bostonian now. I’m also almost seven months pregnant, so it’s decaf for the time being.
What time do you get into the office?
While it’s uncontroversial that “team time” is important, I’m a big believer that focused thinking time is crucial. Instead of wasting time in traffic, I try to take care of the emails in my inbox at home before getting in the car and am usually in the office around 9:30 AM.
Every day is different, but can you outline what a typical day looks like for you?
Morning: Sleep is non-negotiable. I sleep at least 8 hours a day and am usually up around 7am. I usually hit the road by 8:45 AM. Our team has a daily standup meeting first thing when we catch up on everyone’s goals and progress. Then I’ll usually either have meetings with other teams or informal ones with my team to give feedback or direction on what they’re working on.
Afternoon: I try to set up some time for dedicated thinking in the early afternoons and catch up on reading technical papers, industry news, and emails. I like to check out our team’s analyses and get lost in our dashboards a bit to stay fresh on the latest findings. I also have many meetings with senior leaders across the organization to keep them up-to-date on the status of the fleet and other important metrics.
Evening: I’ll usually leave the office a little before 5pm and wrap up any threads for the day, get to Inbox Zero, and log off completely some 40-60 minutes after I get home. Then it’s time to play with the dog, the cat, have dinner with my family, and enjoy each other’s company.
What time do you head out of the office?
I try to beat the heavy Boston-area traffic in the afternoon as well, so I’ll usually try to head out before the big rush hour traffic in the afternoon. I’ll then put in another hour or so at home to catch up on emails and other tasks, where the only distractions are the dog and the cat. My philosophy is that if the work is getting done and we’re meeting (and often exceeding) expectations, I see no reason to keep office chairs warm given all the VPN luxuries we have available to us in a 21st century tech company. Obviously, there are times when unforeseen needs come up and we have to put in a bit more time in the office, but with good planning and preparation, those are thankfully few and far between.
Do you log back in at night or do you shut it down completely?
Unless there is a very good reason to log back on, I try to stay logged off once I’m done for the day. I seldom feel externally pressured to log back on, but I love what I do—so if the mood strikes and I’m struck with a good idea, I’ll take advantage of the inspiration while it’s there.
Any productivity hacks?
Like I said above, sleep is non-negotiable. This isn’t usually thought of as a productivity hack, but since I’ve gotten fanatical about protecting my eight hours of sleep I have gotten so much more productive and successful professionally! You can’t force your brain to be brilliant, but you definitely can stress it so much that it is unable to come up with any good ideas. However, I have heard that the whole “new parent thing” might have a material impact on this for the next several months. Another thing I’m zealous about is running two miles every weekday. I can’t say I manage to do it every day, but I’m pretty good about doing it at least four days a week.
In terms of more “conventional” productivity hacks, I try to get to Inbox Zero every day. I’ve been doing this for about 10 years. I also always leave my phone on silent and have almost no notifications turned on. The only alerts I get from my phone are calls from my family and calendar notifications.
What are the 3 apps that you can’t live without?
1Password, Nuzzle, and Overcast.
I’ve been using 1Password as a password manager since 2006 and cannot imagine living without it. It is the first app that I install on any new device, and it makes me feel a lot safer and saner as I navigate the interwebs.
I have a Twitter account that I use quite a bit because of the strong data community there, but I hate the overwhelming noise that comes with it. To tame things I have hundreds of muted terms that keep the timeline healthy for me. In order to stay up on the conversations, I rely on Nuzzle to aggregate news, articles, and links my network is talking about in a dedicated stream.
I also love listening to podcasts on my commute; Overcast is a great player with tons of useful features like voice boosting and distortion-free silence shortening. If you’re looking for great podcasts to listen to, I really like Linear Digressions, 99% Invisible, Reply All, and Note to Self.
What professional accomplishment are you proudest of?
I’ve been incredibly lucky to have many opportunities come across my path that I’ve been able to jump on: it was the honor of a lifetime to fly to Geneva and present at the WHO, it was an amazing distinction to be part of the team that won the 2015 INFORMS Edelman award, and I still can’t quite believe that I have almost 50 patent applications for inventions I’ve been involved in developing. But my proudest accomplishment professionally has been building the Data Science team at iRobot from the ground up, as we’re starting to see the seeds of all that hard work start to bear fruit and impact the whole organization.
Who do you admire or call upon for professional advice?
There are so many people whose insight I rely on, I don’t think I can pick just one name. It takes a village and, in alphabetical order (because I couldn’t possibly figure out how to order them in any other way), I am often reaching out to Chris Albon, Husain al-Mohssen, Mara Averick, Ed Cuoco, Ben Kehoe, Jack Kloeber, Hilary Parker, Jason Emory Parker, Mark Potter, Mikhail Popov, Andrew Therriault, and Mona Vernon. This amazing group of people helps me with their expertise in data and data science, math, statistics, analysis, business, management, strategy, and life.