A string of Bowdoin connections helped pair recent graduate Chunyi Zhao ’15 with Sajjad Jaffer ’95, managing partner at Two Six Capital, a San Francisco-based firm that applies data science to private equity investing. This spring, Jaffer and Zhao sat down for a conversation about why data science matters and why the liberal arts matters to data science.
Zhao: I first heard of Two Six Capital from Todd Herrmann ’85 [associate director of Bowdoin’s Career Planning Center]. He mentioned there is this really cool firm that does data analysis and applies it to private equity, which just immediately got my attention. So after asking around—especially consulting with [Assistant Professor of Math] Jack O’Brien, who confirmed the data analytics capability of the firm—I just desperately wanted to join Two Six.
Jaffer: My classmate Kevin Petrie ’95 spoke very highly of you. Since Kevin knew what we were doing here at Two Six, he thought you’d be a natural fit. And Jack [O’Brien] really gave me a good understanding of your background. Having a common math professor in the discussion definitely made the right connection for us.
We had a white-glove experience working with Todd and the Career Planning center, especially when I compared it to the headhunters. Not even a question. We think that the introduction through Jack—and then Todd giving us behind-the-scenes, individualized, prequalified screening—was hugely, hugely helpful.
Career Planning plays a unique role in connecting us to future recruits. Sally Li ’18, who is going be our next intern this summer, will be a great addition to the firm. Do you want to talk about Sally and how she joined—how we got to know her?
Zhao: Sally and I were good friends at Bowdoin, and I knew her interest in computer science and math. She plans to be a double major in CS and mathematics. I thought it would be a good opportunity to introduce her to the tech culture on the West Coast. So while she was with the Bowdoin West Trek tour in San Francisco, I took her to the office and introduced her to the Two Six team. I’m very happy that she will be joining us in the summer.
Jaffer: So am I. I think Sally made an interesting comment—that of all the tech companies she saw during her Silicon Valley trek, Two Six was the one place where she felt the company spent less talking about how cool their cafeterias were and how big their gyms were, and Two Six just focused on, “Here’s how much you could learn if you came and worked with us.” That’s just a great way to think about career planning.
What is it about your background and studies that made you a good fit as a data scientist at Two Six?
Zhao: I was a math major and my focus was on computational statistics and data analysis. From day one, I was playing with real data and doing all kinds of cool visualizations, which prepared me well for the real world. Also, my honors thesis strengthened my ability to tackle complex projects independently and methodically, which helps me a lot in my day-to-day work here.
Jaffer: I think the essence of your education comes down to Jack [O’Brien]’s approach to teaching students how to ask the right questions, and the habit of teaching yourself and finding coding resources on the fly. Coding is the new creative writing. I think that’s what made you such a great fit. Coding is the new creative writing, and your liberal arts background has given you the tools to be not only successful, but a leader among others more technically trained.
Bowdoin played a huge role [for me, too]. I was a double major in computer science and government and a minor in econ, and I think that has been the hallmark of what we do at Two Six—connecting the dots between unrelated fields to come up with new insights. To me, that’s the definition of critical thinking.
I was a student rep on the Bowdoin investment committee where I got my first exposure to management and investing, and that was useful in developing a context while in college. I was also fortunate to have very involved mentors after graduating. I was fortunate to have very involved mentors after graduating—in particular, two Bowdoin trustees, J. Crandall ’76 and Dave Brown ’79, who were highly supportive when Two Six Capital was an investment thesis filled with pages of math and statistics.
Lastly, I think of role models from the general Bowdoin alumni body. In particular, I think of Reed Hastings ’83 from Netflix, Bob White ’77 of Bain Capital, Stan Druckenmiller ’75. They all inspire how we think about data science and investing here at Two Six Capital.
What do you find surprising about your Bowdoin education and how it prepared you for your current position?
Zhao: I think it comes down to three things. The role of a data scientist requires a person to know theoretical, mathematical, or statistical knowledge, and you also need to learn a lot of technology on the fly. I found myself at ease learning new things within a week, mastering a new language within days, which prepared me well to facing the different challenges that comes up every week. The second point is that I found myself at ease talking to different people, including our clients, seasoned investors, and Ph.D.-level technology leaders. The ability to communicate clearly and thoughtfully with a different audience definitely gives me an edge in my day-to-day work. Third, is just the courage and mindset to always ask questions and ask the right question, and how much that helped me to solve the problems I encountered in my work.
Jaffer: I’ll give two particular instances about what I found enlightening about how Bowdoin prepared you. We have another colleague of here at Two Six who went to the same high school as you did. She was a math and physics Olympian from China’s top engineering school, Tsinghua University. What I find interesting is you are at par with each other. She may have gone to Tsinghua and you went to Bowdoin, but you both converge at the same place, having comparable skillsets. It tells me that Bowdoin continues to have an edge and can go toe-to-toe with the top engineering schools around the world and produce a great performer.
I think if you take that example, the uniqueness about Bowdoin, again it goes back to asking the right questions and functioning well in a fast-paced, independent learning environment. I think those are the hallmarks of the Bowdoin educational experience.
Zhao: I agree.
Jaffer: Let’s talk about a project that the two of us we have collaborated on. I’m thinking of an example of how you have now worked on two private equity due diligence opportunities very closely with me. The first deal happened when you had just joined, two weeks into your career at Two Six, and it was not a trivial project. It was a $350 million due diligence opportunity with one of our most strategic and well-respected private equity sponsors. The project was about two weeks in duration, and we basically learned by doing. That was part of our on-boarding process. There was no formal training program. You learned on the job where I was the project manager checking each cell in the spreadsheets to make sure that the analysis was correct, and giving you constant feedback. As you’ll recall, the first week didn’t go really well.
Jaffer: But it didn’t surprise me because the complexity of the analysis required was significant, and everybody who came before you we would have put in a similar situation would have failed. So would I. But I gave you the time and the space to adapt and grow so that when the project went to the second week, you effectively took charge and started coming up with new, unique insights about the data that has contributed to some of the intellectual property we have developed here at the firm. So it tells me the learnability, that your self-paced learning environment you had at Bowdoin prepared you well once you learned from your mistakes.
And then I think we emphasize structure a lot here and how to do quantitative analysis, how to produce deliverables, how to do large-scale statistical analyses, proving or disproving investment theses, and then how do you present those finding to investment committees. These are all areas where we have collaborated closely in the last year or so since you’ve joined the firm. That’s great.
Let’s talk a little about “big data.” It’s a term that’s casually tossed around in all sorts of contexts, but it’s incredibly nuanced. Why is it relevant to our daily lives? To me, data science—or big data—is really a combination of three things. It’s asking the right questions, which is why you need to have critical thinking capability, and you need to have an interdisciplinary approach by connecting different subjects or majors that may not be related to each other. To me, that’s the most important aspect. Secondly, it’s having this statistical understanding on how to develop models using that data. And the third is having the technology capability to implement these at large scale.
Zhao: Data science is actually very relevant to everyone in this era of technology because, as technology consumers, we are constantly generating data, and then we benefit from the insight that is being drawn from the data we generate. I think this is a very interesting ecosystem.
Jaffer: I agree. Big data and data science constitute the next industrial revolution. We have seen this in Silicon Valley and technology companies, and now we see the same effect in investing and in how capital is put to work. They have the potential to disrupt how strategic decisions are made, how companies invest, and how they generate higher rates of returns. We think we’re in a moment in time here where we know that the amount of data—the explosion of data—is going to continue. It’s a mathematical certainty. The question we ask ourselves is how do we extend this capability we have built in private equity today to other asset classes?
Our firm was founded on twenty-five years of academic research from Wharton, which is where I went to business school. With that lead, the last three years we have participated in over $10 billion worth of private equity buyouts that have closed, including one of the largest of the year in 2015. We’ve built a large-scale data platform that has the capability to ingest an enormous amount of data. To date, we have ingested and analyzed over $70 billion worth of data that spans over 480,000 product SKUs and over 14 million unique customers. We’re humbled by how far we’ve come along, and we’re pretty excited about the future.
An abridged version of this profile originally appeared in Bowdoin Magazine, Spring/Summer 2016.