Linking the Data at LinkedIn

Carson Chen (MSiA ‘19) is a senior data scientist at LinkedIn, a role he was promoted to just more than a year ago. He has been a part of the LinkedIn data science team since he interned there as part of his studies in Northwestern Engineering's Master of Science in Analytics (MSiA) program. 

Chen’s mission is to help manage LinkedIn’s continued evolution as it works to stay relevant to professionals looking to grow their careers. He’s quick to dismiss the image of the haggard data scientist plowing through a huge trove of numbers using difficult-to-comprehend mathematical formulas.  

“The impact of data science comes more from a consistent and timely supply of good, quality data plus well-designed processes to represent the issue of interest," Chen said, "rather than using fancy skills and techniques on a large data set."   

Knowing how to accurately gather data and apply it in the same manner over a long period of time is what gives organizations the information they need to make smart decisions, Chen said.   

He sees that focus at LinkedIn.  

“I really like the aspect of managing the long-term evolution of a product,” he said. “Through data analysis, we distill a lot of insights and formulate many theories of how the product could do better. We then perform experiments, roll out changes, and observe if and how the users react to the changes.”  

A study that ran for five years was published in the journal Science in September 2022 and showcased the sort of results this approach can create. 

LinkedIn partnered with researchers at three elite universities to conduct a data-driven experiment with 20 million users on the role connections make in helping people grow their careers. LinkedIn changed the “People You May Know” section for some users to more distant connections than a control group, which remained the same.   

The study suggested weaker, more distant ties may be more helpful in finding new employment opportunities than close friends. 

That type of work ties directly into what Chen does for LinkedIn. 

“I design and implement the best possible data process to constantly track, understand, analyze, forecast, and experiment all product improvement initiatives so that the product can be optimized iteratively toward its desired goals,” he said.  

Because of the lessons Chen learned during his time in the MSiA program, he is able to more quickly grasp what the data is trying to tell him, which leads him to be able to make more reliable recommendations.   

Beyond that, the soft skills he learned in his MSiA classes help him deliver those recommendations more effectively, he said.  

"My data science foundation is strong and well-rounded because of my education,” Chen said. “MSiA helped me develop great knowledge that I can always leverage as I continue to learn and grow professionally."