Driving Uber's Safety Mission 

Jake Atlas (MSiA '20) is determined to make Uber rides as safe as possible for both passengers and drivers. 

Atlas is an applied scientist on the ride-sharing company's safety team, where he works at the intersection of machine learning and product development. To succeed, he applies lessons learned in Northwestern Engineering's Master of Science in Analytics (MSiA) program — now known as the Machine Learning and Data Science (MLDS) program.  

“I get to work on challenging problems with tangible impact,” he said. “My work has far-reaching implications. I know my job contributes to the overall welfare of people across the globe.”  

Uber has sharpened its focus on safety since the company's founding in March 2009. In April 2024, Uber rolled out a major new safety program in 12 cities that cross-checks riders’ identities against trusted third-party databases.  

Those successfully completing the check receive a blue verified rider badge, a feature that drivers both want and like. 

Atlas is at the center of these safety efforts. He started with Uber in June 2020 and has been promoted twice since then. 

“I’ve transitioned into a much more autonomous role in which I have the added responsibility of driving the longer-term vision for the products I work on,” he said. “This has made it increasingly important to deeply understand the business side of my work.” 

His time at Northwestern gave him the foundation for that understanding.  

Picking the MSiA program wasn’t a hard decision for Atlas. He earned his undergraduate degree at Northwestern and already interacted with numerous professors from the program. Dan Apley, who teaches the program's Predictive Analytics II course, was Atlas’ undergraduate adviser.  

His respect for those professors made the MSiA program his top choice. The relationships Atlas built with them helped him secure extracurricular work opportunities and enroll in graduate-level courses as an undergraduate.  

“It’s hard to distinguish yourself during the job search, especially someone as early in their career as I was,” he said. “But MSiA promised – and delivered – enough hands-on industry data science experience to help me reach even my loftiest goals.”  

The goal now is making the Uber experience as safe as possible.  

His job is a variety of technical work and business communication. Among other duties, he leads statistical analyses to measure the expected impact of solutions his team creates.  

"My project work spans the end-to-end machine learning development process," he said. “While others may focus on preventing car accidents, my work focuses on minimizing the risk of negative interactions between people getting into a car together.” 

Foundational courses in the MSiA program make that job easier. The most important class that applies directly to his job is the program's Databases/SQL course, he said.  

“I use SQL on a daily basis, without fail,” he said. “Anyone could play devil’s advocate and say, ‘But SQL is mostly plain English. Anyone can learn it.’ Sure, but can you learn it in time for summer internship interviews?”   

That course helped Atlas secure a data analyst internship with Uber, accelerated his learning process, and made him more competitive for full-time job opportunities.  

Predictive Analytics II taught Atlas how the most common supervised learning algorithms work. Generating Business Value with Analytics showed him how to become indispensable as a data scientist. 

"I cannot emphasize enough the importance of communication with both technical and non-technical stakeholders," Atlas said. "The best way to distinguish yourself and make a meaningful impact at work is to be able to explain your methods and persuade your colleagues to act on your recommendations."