One of the fun things about being a leader at a hyper-growth company is that you don’t just have the opportunity to change things — you must drive change to keep up. And working in the new and rapidly evolving field of data science (DS) entails another level of rapid change. We are evolving within the company and as an industry in parallel.
At Airbnb, we think of data as the voice of our users at scale. Our goal is for data scientists to maximize their impact and to look forward to coming to work. Achieving this goal is a work in progress, and we’re continually looking for ways to improve. We recently established a role-defining framework as a part of this evolution. My hope is that what we've learned along the way can help other companies be strategic in defining data science roles.
The main takeaway I will share is that companies consider three tracks of data science work to meet the needs of your business — Analytics, Inference, and Algorithms. Below I'll describe the evolution of how we came to these three tracks of work and how it helps.
A Data Scientist by Any Other Name...
We started off as the “A-team” — Analytics team — with the first hire an “Analytics Specialist.” In 2012, I was hired as a “Data Scientist.” Later, we hired a “Data Architect,” to tackle data quality, then a “Data Analytics Specialists” to help solve gaps in data access and tools. Then we saw additional needs in Machine Learning so we hired “Machine Learning Data Scientists.” These title evolutions were both reactions to team needs and also to the competitive landscape. We became the “Data Science” function in 2015, though we still use “A-team” because it’s fun and has a history we value.
When I took on leadership of the data science function in mid-2017, we had about 80 data scientists, embedded on various teams. Some were building dashboards, some were building NLP (natural language processing) models, others were building models for decision making and designing experiments. The landscape was incredibly varied.
This variety isn't totally unexpected. Data science is relatively new and growing rapidly. We see this in the data. First, looking internally, we see that applications to data science roles at Airbnb have grown 4x between 2015-2018 --
(Of course, this is also driven by interest in Airbnb and many other factors.)
And, according to Google Trends data, queries for data science have also grown (link):
The increase begins in 2012, now 4x in six years.
Not only is a new field, but what people mean when they say “data science” is also incredibly varied. Sometimes it is purely machine learning. Sometimes it’s business intelligence at a tech company. It's new, and it's evolving.
We found that expectations weren’t clear about what Data Science worked on.
The downside of this variety within a given company is it can result in organizational confusion and churn, since partner teams don’t know what to expect from data scientists, and the data scientists themselves might be unclear on their role. People who come from places where DS do only modeling might not consider it a good use of data science skills to do more simple analytics. Others from places where DS only does analytics might feel that it is best for engineers to do modeling.
We also had an additional challenge: team members who were doing analytics work felt like their work was not as valued as machine learning work, and yet their work was critical for the business. Business partners craved more actionable insights to drive decisions and expanded tools to understand the data themselves. We had invested in data education through our very popularData University, but we still needed experts.
