Ben Wellington – Complex Feature Engineering at Two Sigma (S7E32)

Hey, everyone. Corey Hofstein here. Before we get started, if you're into capital efficiency and smarter portfolio construction, mark your calendar. The second annual return stacking symposium is coming to Chicago on 10/28/2026, headlined by Cliff Asness, founder and CIO of AQR. It's a full day of conversations on how to stack return streams, put your capital to work more efficiently, and rethink the way you build portfolios. Whether you're an advisor, allocator, or just curious about return stacking, this is the room to be in. Head to www.returnstacked.com/symposium to learn more and grab you

I'm ready.

Alright. Three, two, one. Let's jam. Hello, and welcome, everyone. I'm Corey Hofstein, and this is Flirting with Models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

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My guest this episode is Ben Wellington, head of complex feature engines at Two Sigma. In a modern quant process, one might argue that edge can live in three places. The data you can get your hands on, what you do with that data, and how you forecast from it. Ben lives squarely in the middle layer, feature generation, which also happens to be the place AI is reshaping the world fastest. So that's where we spend our time. We start with what a feature even is and why as raw data gets commoditized, the edge increasingly comes from what you build out of it. Then we follow the thread running throug

I'm happy to be here. Thanks for having me.

So before we dive in, maybe we can give the listeners a little bit of lay of the land and your background and who you are and would love to get a sort of sense of where you went to school and how you ended up at two Sigma and what the beginning of your career looked like, and take us, you know, the quick run through to where you are today in the complex feature engines team.

Yeah. I did my PhD in natural language processing at New York University with it really a focus in machine translation. So you have to kind of go back in time a little bit to think about the fact that it was still quite odd to be, say, speaking to a computer or using translation or before Siri, before Alexa, all this stuff. And it just sounded like a cool field. So when I was looking to go to grad school, because I didn't know what to do with my life, I read some research areas and it just seemed it just seemed fun. So dove into what, I guess, fast forward today, turned out to be a pretty cent

So your title today is head of complex feature engines and right in there is that word feature and that word gets thrown around a lot in the world of quantitative investing. But at least anecdotally when I talk to many people, it means different things to different people. And so I would love to maybe just start with you. What does the phrase feature mean? How does two sigma really use that word? And maybe if you can give us some examples that span the spectrum from the very familiar perhaps to the very alternative.