Driving Business Efficiencies at Uber Freight

Find out how Columbia Business School graduate Bar Ifrach, PhD ’12, now senior director of applied science and head of marketplace at Uber Freight, uses data to solve high-impact business problems.

Data & Business Analytics

Increasingly, MBA students are looking to deepen their technical skills as they seek out roles in data-driven businesses, particularly in the technology space.

This has led to the popularity of courses such as Python for MBAs, which helps to prepare students to work alongside data scientists to understand how customer analytics affect a business.

We recently spoke with Columbia Business School graduate Bar Ifrach, PhD ’12, who in his current role as senior director of applied science and head of the marketplace team at Uber Freight is focused on using data to help shipments move around the country as efficiently as possible in a massive $700 billion industry.

Watch the interview above and read the transcript below.

Q: What inspired you to pursue a career in data science?

Bar Ifrach: I got my undergraduate degree in economics at Tel Aviv University in Israel, and I had a real infatuation with the academic side of it in my last year in college. And during that year I decided that I wanted to get a PhD in the field, and all my professors at Tel Aviv University had their PHDs from American schools, so I thought that must be the right way to do it, and I was lucky enough to be admitted at Columbia Business School.

Q: Tell us about your role at Uber Freight.

Bar Ifrach: I’m both heading the data science and applied science team, but also heading the marketplace team. The marketplace team is essentially responsible for connecting the demand and the supply, so shippers and truckers, or carriers, and we primarily work on pricing. We set the algorithmic pricing in the business for every load that we touch and that, of course, has a really strong implications for our business outcomes—thinking through how we build our unit economics and how we flex between margin and growth. And that's been a really interesting experience: owning both a concrete business outcome as well as the data science function.

Q: How are you using data to improve business outcomes?

Bar Ifrach: Something we're using data to understand better is the entire logistics business cycle. I'm sure that everyone got their chance to read about how supply chain during COVID has been disrupted, and how rates of shipments and costs of shipments have skyrocketed. We saw a market that was trending up in terms of costs, a lot of constraints on supply, and now we are seeing some of those bottlenecks being released and rates coming down.

One of the things that we're doing on my team is understanding how demand and supply in the market interact to determine the equilibrium prices across the U.S. primarily. So we're looking at a lot of interesting models that harvest external data, but also internal data, to determine what is essentially the supply curve, namely at what level of prices in the market we'll see more trucks coming in, more carriers coming into the market, versus leaving the market and therefore pushing the prices down or up. The equilibrium point is always a moving target because both supply changes, the underlying costs of running a truck change, as well as the demand driven by consumption and now inflation, and these factors keep on varying. So we are very interested in that problem and spending a lot of resources to better understand it.

Q: What skills do MBAs need to be successful in data-focused roles?

Bar Ifrach: If you think about how tech companies operate, it's really the core unit of tech execution—what we call the cross-functional team. So already by the name you can understand that you'll have very different skill sets there. You typically have a majority of the group being engineers, then you have data scientists or data analysts or applied scientists, depending on the problem, and designers, and then typically a product manager. Of course, proportions can change depending on what you're working on, and within that group data plays a big role. But everyone has somewhat of a different perspective and a different language for how they think about the problem.

I'd say the important part for MBAs going into that environment—and typically they go into the product manager role—is to really be able to adapt their language to those different skill sets and be able to bring the most value out of each one and out of the team as a whole. So oftentimes what I see with product managers who build their experience and become really fantastic is that they're able to actually get the team to work well together and that's by elevating those skill sets, creating an open environment where the best ideas can float and have the most impact because you don't know if that will come from the engineer, or will come from that designer, or come from the data scientists or will come from you. Creating that space is really important.

Read our 2022 alumni magazine profile of Bar Ifrach.

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