In an interview with Alok Gupta, a Data Science Manager at Airbnb and former algorithmic trader, I learned about the introspective efforts the company has made to scale its rapidly growing data science team into what it is today and how they (and other data teams) face the future.
While the evolution of the team’s organizational structure has permitted Airbnb’s data scientists to flourish, the company’s level of accomplishment derives from a “laser focus” on two things: truly caring for their employees and making highly intentional data-driven decisions. Whether it’s developing open-source tools for reproducible research or striving to improve the status of diversity in data science, Alok makes it clear that Airbnb pursues efforts which converge on these two guiding principles.
Hypergrowth: Scaling from 5 to 70+ data scientists in a few short years
In 2013, Airbnb had a small, centralized team of five data scientists serving the data needs of the company. Since then, they have grown to become one of the largest, most innovative startup teams with over 70 data scientists now serving separate business units. In addition to setting a consistently high bar on new hires and focusing on technical mentorship from peers, the structure of the organization has been key to successful growth.
Alok calls the transition from a centralized team of data scientists to smaller embedded teams which sit within product areas “a breath of fresh air” where they work as business partners with their teams. Compared to the previous structure, he says the new model has been “very powerful” for the company.
This transition has happened in tandem with the evolving notion of what it even means to even be a data scientist. Many likely agree with Alok when he calls it an “overloaded term right now.” He spells out what he believes are the four or so specialized roles that better articulate the work done by those of us that aren’t data science unicorns:
- Data engineers – They take messy data and transform it for analysis.
- Product builders – People who build data products that are user-facing. For example, they may build a recommender engine.
- Data analysts – They provide chief analyses outlining where opportunities lie for the business.
- Data experimenters – Scientists who know how to design and perform an experiment.
How has the data team been able to grapple with the growing pains that accompany such quick expansion? The very creation and transformation of the data science team at Airbnb stems from the company’s position at the extreme ends of two spectrums, Alok tells me.
First, Airbnb sets itself apart as a company that goes out of its way to ensure its employees are happy, successful, and cared for. For instance, their investments in data bootcamp for onboarding, peer mentorship, and conference participation among other initiatives are all important ways in which Airbnb cultivates its employees.
On the other hand, Alok emphasizes that Airbnb is very metrics and goals-driven when he says, “Everything we do is very deliberate, very quantitative, and laser-focused on our goals.”
Along the second continuum significant to the company’s operating philosophy, Alok emphasizes that Airbnb is extremely metrics- and goals-driven when it comes to making business decisions:
“Everything we do is very deliberate, very quantitative, and laser-focused on our goals.”
The message is that Airbnb has, at least in part, made such a commitment to the quality of its data science team in the first place as a means to substantiate its research-driven mode of conduct.
In the rest of our conversation, Alok shared with me how Airbnb achieves success, cohesion, and better outcomes for themselves and their users as a data science team. By thoughtfully positioning themselves as a company that cares about its employees’ well-being as much as metrics-driven decision-making, it’s apparent that Airbnb makes progress through the marriage of both.