Mason Lender: Who Finds the Unicorns?

By Stella Haffner   |   June 25, 2024
Mason (right) presenting with his business partner, Nicolas Neven (courtesy photo)

This week I spoke to Mason Lender, the 23-year-old founder of an alternative investing company powered by artificial intelligence. Mason grew up here in Santa Barbara, attending Crane Country Day School and Santa Barbara High School before packing his bags to start as an undergraduate in Statistics & Data Science and Global Affairs at Yale University. In our conversation Mason explains how he found his way into the world of financial tech, how his mind works as a young entrepreneur, and what his company Vantager means for private market investing.

Q. How did you choose to study data science? 

A. I was always obsessed with probability and prediction. Growing up, I was fascinated with different probability games. My family played this one called Liar’s Dice, and I was just fixated with it. I would try to find a way to have an edge on my family members, so I could win and choose what we ate for dessert or whatever the reward was. That’s when I remember starting to think about probability. I would look at the dice and think: “Okay, there are six sides to this die, and all of us have five dice under that table, that means if everything is truly random, I might expect these many of a certain die to be under the cups.” With more practice, I started thinking about other factors, like the fact that my brother doesn’t lie, and he was therefore likely to give certain answers. I kind of created some methodology to the madness.

It sounds like you were putting together a type of statistical model.

My mind kind of works like that. For every decision I make, I attribute an expected value. When I think of the stakeholders in my company, my family members, my friends, my girlfriend – I try to create the best outcome. What decision will lead to the highest expected value for me and everyone else around me? It’s an optimization game. Every aspect of my life is like that. In high school, I loved sports and played tennis. There are a lot of probabilities and statistics that exist within tennis. Thinking about it this way consumes me. It’s how I live. 

So you have been developing this style of thinking and reasoning since you were a kid – did anything change when you started college? 

I looked for areas where I could flex my mind and train it as much as I could. I know I wanted to work in a field or in a job where I could use statistics, probability, and data science as much as I could. I took four internships during high school and college to be able to learn more about this area. My first big step into the finance world was my internship with McKinsey where I really got to start developing my skills for the first time. There I learned about how large organizations worked while working with a large client. They had revenues of a billion dollars, and here I was as a college student helping them target strategic mergers and acquisitions. I realized I could bring my data background both for prediction but also for data visualization to make more informed decisions. You can tell so many stories with the same data.

It sounds like you were given a lot of responsibility in these internships! You must have felt well prepared to be independent by the time you were graduating. 

After that summer at McKinsey, I thought: “Okay, I have learned all these different things, I am going to use my last year here at Yale to explore and find out what makes me tick.” So I started doing lots of reading and research on startups and venture capital investing. This was partially inspired by my time at McKinsey, but also just as a kid I had always been fascinated learning about the world of startups. You hear different stories about people dropping out of university and building these massive companies from their garage. So I started doing my own research and decided I wanted to write my thesis at Yale leveraging statistical predictive analyses. I read a great book by Ali Tamaseb called Super Founders: What Data Reveals About Billion-Dollar Startups. It focuses on analyzing Unicorn Founders. A Unicorn Founder is someone who has built a company that is worth over a billion dollars. Tamaseb’s analysis looks at how they are different from other founders.

You mean an analysis of their personality?

More of a holistic analysis of the individual. There are these notions for instance that successful startup founders drop out of college. People might think, “Oh I have to drop out of college to build a company.” I found it fascinating. I reached out and spoke to Tamaseb about some of my own ideas, then I came back and I started to write my own piece, which looked at the other side of the table. I looked at venture capitalists themselves and asked: Which venture capital partners are actually outliers in identifying incredible startups? Who finds the Unicorns? 

In my research, I learned a lot about myself and my capabilities. Producing a research paper like this isn’t an easy feat, and at times you’re banging your head against a wall, and you want to stop. But you’re driven to continue. I set out, and I wrote this piece, and it was eventually published on Crunchbase.

That’s amazing! So other people can go read about your work?

Yes, and I have actually gotten a lot of interest in my research. After publishing the article, I spoke to many venture capitalists who were interested in the research. And my professor, with whom I was doing the research, introduced me to a partner at a venture capital firm called Entrepreneur First. We got coffee together while I was still in school, she told me about what they do, and I was enthralled. 

Entrepreneur First is a Europe-based firm that prides themselves on being the first investor in exceptional talent to build startups from scratch. They bring talent together to develop their most ambitious ideas and provide them with support to foster that. I had some chats with them and some interviews and eventually I decided to join their program.

So what happened next?

I moved to New York to be a part of their first inaugural U.S. cohort, where my co-founder, Nicolas Neven, and I began building Vantager.

How did you two actually come up with the idea for Vantager?

I was always still working with the models and the data I had scraped from my thesis. When Nico and I talked and I learned about his background in artificial intelligence, we realized there was a big overlap in our interests and a potential whitespace, and we decided to run with our idea. 

Our mission for Vantager was to make safer private markets for investors. We leverage artificial intelligence to augment investment teams through bespoke risk assessment, due diligence, and reporting, so that they can work with greater confidence and efficiency.

What does that mean – augmenting investment teams?

We are augmenting but not replacing. Investors don’t want somebody to do the job for them because ultimately, they need to make the decision. Vantager’s AI supports them in their unique processes by leveraging the data they have to operate optimally.

What has been your biggest challenge in being a founder?

Every day is so different and brings its unique challenges. You have to work very hard to create the future you dream of. But as difficult as it is, it’s exciting. Vantager has gotten a lot of interest and has changed greatly since its inception. We’ve been working with some of the largest asset allocators on building out Vantager. The work is never finished, and we are using these feedback loops to make a better product. It’s been an incredibly rewarding process.  


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