#1 LinkedIn, PYMK, Machine Learning and Full Stack Teams

In 2006, the company LinkedIn launched a new feature called “People You May Know”. This “prompt” turned out to have 30% higher click through rates than any other prompt in use at LinkedIn. It created millions of additional views and connections. The team also went on to create a bunch of additional machine learning products and helped to foster a deeply data-driven culture at LinkedIn.

 

In their journey LinkedIn uncovered two important principles. The first principle is well explained by DJ Patil:

 

“After all, what is a social network if not a huge dataset of users with connections to each other, forming a graph? Perhaps the most important product for a social network is something to help users connect with others. Any new user needs to find friends, acquaintances, or contacts. It’s not a good user experience to force users to search for their friends, which is often a surprisingly difficult task. At LinkedIn, we invented People You May Know (PYMK) to solve this problem” (DJ Patil, 2011, “Building Data Science Teams”)

 

As far as I can tell, LinkedIn was one of the first companies to understand this. Shortly after Facebook followed up in 2008

 

Interestingly, what LinkedIn also learned in the process, is how to organize data science or machine learning teams in general. This quote explains it nicely:

 

“However, they were uniformly discouraged. They did first-rate work that they considered critical, but that had very little impact on the organization. They’d finish some analysis or come up with some ideas, and the product managers would say “that’s nice, but it’s not on our roadmap.” (DJ Patil, 2011, “Building Data Science Teams”)

 

So what did LInkedIn do? They did exactly what needs to be done in this situation: Put the responsibility onto one spot & decouple the teams. They created a full stack team which had everything in it to create their own roadmap. 

 

The results were simply amazing, the products like “PYMK”, “Who Viewed Your Profile” and the Career Explorer all work on the essential components of LinkedIns “graph” and thus leverage LinkedIns strengths to the core.

 

Principles in Use: Leveraging Machine Learning along the value chain, Organizing Machine Learning Teams for Success.

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