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How Applied and Data Science leader Minji Lee is driving innovation at Uber Freight

November 3, 2021 / Global

by Amy Contreras and Chelsea Kelly

Minji Lee leads the Applied and Data Science team for Pricing at Uber Freight. Below, she gives us a behind-the-scenes look at her career so far, the lessons she’s learned, and what continues to excite her about Uber Freight.

Tell us a little bit about yourself.

“I lead the Applied and Data Science team for Pricing at Uber Freight, which covers both carrier pricing as well as shipper pricing. I started my career at the World Bank, an international organization in the nonprofit sector, working with country-level macroeconomics data, and then moved to tech companies where I worked with millions of user-facing data points. I originally joined Uber in 2015 and worked on the mobility business, left the company at the end of 2017, and have now joined again. In between my time at Uber, I was at a couple different mobility and logistics companies, which has been a consistent theme in my career.” 

What excites you about Uber Freight?

“I came back to Uber because I was excited to work on the Uber Freight offering. One thing I love about Uber Freight is it makes you understand things behind the scenes. There are many things that we take for granted, such as how goods get delivered to our doorstep or get stocked at a grocery store. At a high, abstract level, we know there’s a delivery truck that does the job, but not many people know the inner workings of logistics. After getting into the freight industry, I have more gratitude towards everyone involved in the process. There is so much interesting data science work to be done that is unique to the industry.”

How would you describe the culture at Uber Freight?

“Uber Freight is innovation-driven. With Uber Eats or the mobility business, the business model itself was completely new. Uber Freight is a little different because there are already successful, publicly-traded freight brokerages out there that have been around for decades. With Uber Freight, we know the business model is there and that it works, but it’s really about how we introduce technology and then make it efficient. Uber Freight is a young company, but we’ve already achieved a massive scale. We want to become even bigger and more efficient, so the culture is very focused on how we innovate and bring the technology to scale.” 

Tell us a little bit about some of the interesting problems or the things that your team is working on in data science.

“Uber Freight is a digital brokerage, so we connect shippers to carriers. My team is in charge of coming up with the pricing algorithms to make this marketplace efficient and scalable. There are different branches of data science: analytics, statistical modeling, machine learning, optimization. We have to employ all different sorts of data science techniques, and that gives us exposure to a wide breadth of areas that any data scientist can learn from. I think that’s something very unique about our work as well as our marketplace.”

What are some of the lessons you’ve learned since working at Uber Freight, trying to tackle some of these hard challenges?

“One is about the algorithms we come up with—even though they’re very data-driven and sophisticated, they’re also not perfect. Just like humans are not perfect, no algorithm is perfect. I joined Uber Freight during the pandemic. The pandemic has been such an odd time—in the logistics industry, we’ve seen huge market swings month over month. We couldn’t have predicted COVID, nor could we have predicted all the volatility that came with it. The question is not just about making the best predictions, but rather about risk management and how we can better prepare ourselves for future uncertainty. 

Another challenge I’ve had is about bringing innovations fast to the market. The Silicon Valley mantra is ‘fail fast.’ While we do have to move fast, we also have to think about how to build systems that will be resilient and last for years. The same challenges apply to the algorithms we develop. We want these algorithms to be adaptive but also sustainable over a long time horizon.”

What are some reasons why someone should come build their technical career at Uber Freight?

“It’s a little bit cliché, but I think it’s really the people. The people leaders that I’ve met at Uber Freight are really good humans. They have their team members’ best interest in mind and truly invest heavily in their growth. That doesn’t necessarily just mean promotions. If a person wants to work in a different area that isn’t within their purview, managers help the person to achieve this. Ultimately we do what’s best for the business, but that doesn’t have to conflict with individual growth.

That’s the foremost reason for choosing Uber Freight and then, of course, how fascinating the industry is and the data science problems that we have to solve are.”

How would you sum up your experience at Uber Freight in a sentence or two?

“Ultimately, in your career, you want interesting problems to work on and you want to work with good people. Of course, the company’s growth potential also matters. I think it’s really rare that you have all those components in one place, and I think that’s something that Uber Freight offers.”

If you’re interested in joining us, learn more about Uber Freight and explore our open roles →