Harnessing Data, Diving Deep, and Connecting Networks: Insights from Matt Ober of Social Leverage

Matt Ober, General Partner at Social Leverage, delves into the world of data, automation, networking, and early-stage investing.

Matt shares his journey from specializing in equity derivatives at Bloomberg to hedge funds and then leveraging his expertise to navigate the complex waters of venture capital.

00:00:52 Matt Ober shares his background from Bloomberg, WorldQuant, Third Point and the journey into fintech and venture capital
00:02:14 Scaling WorldQuant 100x with data
00:07:02 Moving to venture capital from hedge funds
00:08:42 Why is data so important in fintech - access to information... and financial markets?
00:09:56 AI is the next frontier
00:14:52 What are the opportunities in fintech and web3
00:16:34 Opportunities in wealthtech
00:18:09 Exciting areas for people in their studies or early careers to focus on in fintech and web3
00:19:14 No such thing as the perfect job
00:20:09 Networking as a superpower - social leverage
00:21:18 Follow the laws of karma as you go through your career
00:22:28 Generalist versus specialist
00:25:46 Always be learning
00:26:55 Investing in people at seed stage startups
00:28:29 Summary

Read Matt's insight's here https://mirror.xyz/workinfintech.eth

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Transcript

Mr Matthew Cheung (00:03.094)

Hi, this is Matt from Work in FinTech. And today I'm delighted to be joined by Matt Ober, who's a general partner at Social Leverage, which is a VC firm. He's previously worked in a hedge fund world with Thirdpoint and WorldQuant, and he cut his teeth at Bloomberg. Hi, Matt, good morning.

Matt (00:20.420)

Hey, great to be here.

Mr Matthew Cheung (00:22.610)

So I reached out because you actually shared something on LinkedIn a few weeks back now where you were talking about your kind of career journey and how things have happened through being proactive, through some serendipity, through increasing your surface area of networks and contacts and so on. Can you talk through your background and how that journey has happened and how you have kind of made things happen to get to where you are today?

Matt (00:51.268)

Sure. So I grew up in California, went to California State University, Chico, which is up in Northern California. Did some internships locally there and worked at Wells Fargo as a teller and banker in school and then went to Bloomberg in San Francisco after graduation. Bloomberg, at that time, everybody started answering the telephone. So anybody who dialed the 800 number, they had recent graduates answering the phone.

So we kind of learned the Bloomberg culture and just, you know, all the facets of the business, moving into the analytics group, which is really just answering all those help, help chats. And I'd say that for the five years I was at Bloomberg, it was really just like, learn everything about the markets and start to specialize in areas that interest you. So I stayed in analytics, learned a lot about equity derivatives and charting, just the equity markets in general. And also at the time,

did my c a i a to just really understand alternatives better bloomberg uh... often the opportunity to move to new york and so i transferred to the new york office with them because i kind of realize that i wanted to be in the financial services space i was really the place to be san francisco's kind of a retired wall street at the time and through you know hundreds and hundreds of uh... applications to try and move into a hedge fund i ended up

getting a job at WorldQuant through a Craigslist posting. So I ended up joining WorldQuant back then. It's probably around 60 people based in old Greenwich, Connecticut. And I joined EGORE, which was a small quantitative hedge fund as part of Millennium Partners and was in charge of working on data strategy. What that ended up being over six years was, how do we consume more data than anybody else in the world? And if we do that, we can manage more money.

So we opened up offices around the world, 20 plus countries, just traveling from Russia to China to Thailand, hiring the smartest and best in their local countries. And my job was thinking about how can we automate the consumption of data. So we tried to 100x everything we did every year. And what that meant for Igor was, how do we go from...

working with a few dozen vendors to testing out hundreds a month and paying thousands of them a year. Everything from automating the contract process to the due diligence questionnaires to ingestion, automation, idea generation, all the way into tracking the data as if it's a stock. So instead of watching the stock market during the day, we'd watch the P&L per data set based off of all of the algorithms that we're running using that data.

Matt (03:43.604)

unbelievable time to be there. We recruited 600 people over those six years. I became the head of data strategy. I started the WorldQuant Ventures Unit with my good friend Steve Lau, who's there now, where we invested in a lot of these early stage companies. Companies like ChartIQ and Benzinga, Canelist, but also just technology and companies that we started to use or look at while we were there.

Matt (04:12.888)

being approached by a recruiter and ultimately joined Third Point, which is Dan Loeb's Hedge Fund. I joined there as the chief data scientist. What that quickly meant was rethinking how we do risk data analytics in the entire technology of the firm. So the idea there was how do we build a world-class data science team but continue to do the investment process that Third Point has always been good at, which is...

Matt (04:42.384)

On the equity side, there was part activism, part investing for longer term trends, structured credit, there was private investments, and we also looked at crypto. So we built everything from the ground up and hired a team, brought over people I had known very well, hired some fantastic people out of Faxset and different hedge funds, some data vendors as well.

Matt (05:11.052)

The idea was how do we take data and information and technology and infuse it into third points process? Obviously, that's a big culture change, but over the five years, we went from not really using much data or technology to doing everything from leveraging our own internal research management system to every investment had a large amount of data that helped us understand the investment we're making better.

Matt (05:37.600)

investing in Netflix and there's a whole host of thousands of datasets you could use to investing in a credit investment where we analyze the loan tapes. So it was an unbelievable experience. Did that for five, a little over five years. Rebuilt all the processes. That also meant how we did analytics and reporting internally, the risk systems that we used. I think the opportunity for me was I came in with a...

Matt (06:07.960)

a business plan and Dan's blessing and a desk and nothing else and it was go build. So it was hiring, that was architecting everything. We had to change a lot of heads that were there currently. We had to bring in a lot of new people. But it was very successful and I think we accomplished more than we ever planned to. And always during that time, both when I was at WorldQuant and when I was at Third Point, I had been investing in early stage startups.

Matt (06:37.288)

I had met Howard Linsen because at Worldcom we were the first hedge fund to leverage the Stocktwits data for investing. And Howard was the founder of Stocktwits at the time. They ended up doing some investments alongside their Seedstage fund. I became an LP in social leverage. And then ultimately when I was at Thirdpoint thinking about what was next, joining the team at social leverage was like...

perfect next step for me. So join the team as the fourth partner. You know, we're an early stage seed company. We invest in fintech and enterprise SaaS companies and you know, really are hands on and like to work with the founders and build. And I think that with my background doing that in financial services, seeing it from the institutional side, working with investors was the perfect opportunity. Allowed me to leave New York, come back to the West Coast where I'm from, get to move to San Diego.

Matt (07:36.816)

and now spend my time working with early stage founders and we also have an emerging manager fund to fund so we get to work with other amazing investors that invest in fintech, web3, sass, consumer that we both look at the investments they're making but also are investors in their fund directly.

Mr Matthew Cheung (07:58.734)

So I guess all of your experience is really helping your investor kind of decision-making at the moment. Just going back to what you were saying about data, data's been a big part of everything that you've done. Why is data kind of more important now than it ever has been? Because it sounds like you were there in the early days in the hedge fund world when hedge funds could see

Mr Matthew Cheung (08:26.266)

a big advantage into using data before other people were. And in the, what was it, kind of the mid 2000s, that was really in its heyday. And since then it's become kind of more sophisticated, more complex, more data around than ever has been, but then the tools are there to interrogate that and analyze it in a better way. Why is that data so important? And if people are looking at trading and investing, why and how can they...

Mr Matthew Cheung (08:56.434)

utilize what data is out there and the tools that are out there.

Matt (09:00.160)

Yeah, I mean, listen, I think data has always been one of these things. Investing in general has always been like access to information, right? It's always been expert networks, sell side research, discussions with management. I think now we're just in this digital age where everything has some sort of data footprint and, you know, if you think differently, you can imagine everything is being tracked somehow with some sort of system, whether it's on the web or it's inside of hardware and that data is being collected and put through software.

So accessing data, you know, I've been saying for probably 10 plus years, data is the new oil. I think harnessing that data and making sense of it has always been difficult, and only the largest firms can actually do that. And, you know, quants can run those two machines and do systematic trading, whether it's automating a fundamental idea or coming up with something that's a little bit more random, and it's just pattern recognition. I think now in this world, we have...

so much information and so much data, but we have these AI tools, right? And I kind of look at it as like years back, AWS and leveraging the cloud was a differentiator. Even when I joined ThirdPoint, there was very little of any cloud usage and we stood up an AWS instance so that the ThirdPoint data science team could get off the ground, right? Nowadays, everybody's using the cloud, everybody's using Snowflake and it's kind of table six. I think with AI, you know.

Matt (10:28.340)

It's getting to the point where every pitch deck has an AI strategy. AI is going to become these democratized tools. I think we even heard Google say that they don't have any differentiator or way that they're going to be able to compete, but the way that they're going to compete is they're going to infuse AI into everything that they have. AI is the models and everything is becoming open source. The biggest winners are going to be those who own the data, those who have proprietary data.

Matt (10:56.948)

either a unique UI UX experience or they own pieces of your desktop. In the investment world, you always look at like Bloomberg, like they own part of your desktop. Displacement sales are very hard versus like popping up something new, but there's only so much room, there's only so many screens people have too. So, you know, we've seen over the years other firms whether it's AlphaSense and Tegas and some of these other companies like start to encroach and like grab.

desktop space, but they've had a unique offering. Maybe it was unique content like Tegas or AlphaSense had a unique way of digesting information. Now it's a race to who can consume the most content and data and leverage these AI tools. I just think these tools are going to get better and better and there's a lot of them. Not many companies or people in the world are going to compete at creating better AI. It's going to be the applications built on top of it. I think if you're a company that's been around a long time with a really rich

unique data set that's only becoming more valuable now.

Mr Matthew Cheung (11:57.246)

So as an investor, obviously AI has been around for a long time, right? It's been around since the 50s, but we're beginning to see these tipping points with all these interface moments with AI and chat and so on. As an investor, you will see AI, like you say, on every pitch deck, probably that comes to you now. And quite rightly, because if you're not embracing AI as a company, then you're just going to get left behind. But how and what do you look at as an investor?

when people are saying they're using AI? Is it in a novel way that they're using that AI connecting to a particular data set and application? Or what type of things would, if I'm an early stage startup or if I've got an idea and I wanna kind of start plugging in these tools, what to you would be something that differentiates that pitch deck from a different one?

Matt (12:53.068)

Yeah, I mean, I think we're not AI specific investors. I think when you look at FinTech and enterprise stats, everything's gonna infuse AI into their technology stack and their approach. For us, it's what problem are they solving? Is this a company that has a very unique data set and a data set that has a moat? And then leveraging AI on top of that, obviously there's a million ways to do that. I also think AI is being used to just make teams more efficient, whether it's how they're building their own technology.

how they're doing their coding, maybe they create content leveraging these tools. So I think it's a whole host of ways. I think we'd like to understand why they're using AI and what's the value add, but I think there's this race to get things out there, leveraging AI and everybody doing the same thing. And I think those are harder for us to really decide who's gonna be the winner there, right? So it's more about, you know, what's the unique.

either data offering or problem set that they're solving.

Mr Matthew Cheung (13:56.970)

So going back to the, I suppose, one of the areas that you're now investing in with FinTech, obviously this is the work in FinTech podcast, so we're going to talk about FinTech and Web3 as well. On the FinTech side, what kind of trends and opportunities do you see in FinTech? Because FinTech itself has been around for what, 10, 20 years in various shapes or forms, and we've had the first wave of kind of open banking and payments and so on.

And then the second wave where it's a bit more enterprise heavy and integrations and doing technology solutions for bigger financial institutions as well as consumers. And now we're kind of coming into this third wave where there's crypto, web three and so on. So what do you see as the landscape of FinTech and how do you see web three sitting in that as well? Is that a subset of FinTech? Is it his own area? It should be good to hear your thoughts around that.

Matt (14:53.952)

Yeah, I mean, I think leveraging Web3 or leveraging blockchain at this point is an interesting part of the technology discussion. I think we're more interested in Web3 where it doesn't define the company. We've made investments in companies that are leveraging blockchain because it makes the ticketing industry they're disrupting or they're disrupting verification opportunities and they're leveraging blockchain because it allows them to do things.

cheaper or with a ledger or a whole host of different reasons. I think that that's important. I think there's a lot of fintech to be built that doesn't have to leverage Web3 or leverage really blockchain technology. I think we had this wave of everything crypto and now it's kind of died down and we're trying to find real applications and use cases. So we're always interested in meeting companies that are leveraging blockchain as part of their technology stack. I think those have become more interesting than just Web3 companies.

uh... for web three state that don't have a real reason for using the uh... uh... technology i think for us you know we've had a historical traffic could have been very good on the context and btc being first investors robin hood and the toro you know then moving on to come to like alpaca but it were also we really like the pics and shovels on the btb side you know things that are boring obviously i have being a data background things that are

Matt (16:20.908)

make them more efficient. So, you know, for us, it's exciting to meet entrepreneurs that are building something to solve a problem that they had or something, a pain point that they saw in their previous career. And now we're out building to solve that. We also think in the wealth management side, we think that there's a whole host of opportunity in wealth debt. You know, we had this boom for the last five, 10, maybe more years of wealth creation. And now there's this whole opportunity to manage that wealth.

whether it's individuals that have exited family offices or just people that have had successful careers and never really thought about investment opportunities, whether that's their investing in alternatives and there's passions within the alternative space, whether it's just better access to understanding what's important to them and how to manage their money. So that's something that interests us. And then on the enterprise side, my partner spends a lot of time on vertical SaaS and just thinking about...

Matt (17:18.964)

software for different industries. A lot of those end up having an embedded finance solution, which obviously is something that interests us.

Mr Matthew Cheung (17:28.490)

So if you were a student or kind of early in your career now, given the breadth of opportunity that you see across this kind of ecosystem, what, is there any particular areas which are very hot and exciting right now? And are there any that, you know, the, the old Wayne Gretzky, you know, scape with a puck is going to be if I'm just starting college or high school and you know, when I go to, when I graduate and it's might be, you know, the late 2020s.

what technologies are going to be hot at that point and learning about them now. Have you got any advice for younger people thinking about the longer term view rather than just, oh I need to get a job when I finish university?

Matt (18:11.188)

Yeah, it's funny. It's like going into finance, I feel like a few years ago or even a few months ago, I would say the differentiation for like analysts coming out is like, if you have skills, just understanding how to use Python, right? Like everybody understands Excel, people can get a CFA, they know how to model. But if you have some Python skills, then you can actually leverage more data. I think now, I would say if I was younger, it's like embracing all of these AI tools. It seems like programming is going to be democratized and there's so many tools out there to make...

everybody be able to be somewhat of a programmer. I'm seeing so many things where whether it's SQL or leveraging some of the tools that Amazon's putting out and Microsoft's putting out, everybody should have some basic skills in programming. And then I think these AI tools, the ability to write content and do research leveraging some of the tools that are out there now, I think that if I'm younger, I can be 10x more efficient.

and people that aren't embracing these tools. So I would say embrace that. I would also say like coming out of school, like a lot of people are looking for the perfect job or the perfect thing that they want. Like I didn't know I wanted to work in the hedge fund industry or in venture capital. I didn't really know what Bloomberg was when I got the job there and it ended up being the best thing for me. I also think that going to a place that really trains you and you learn, right? Like it's hard to like think coming out of college that you know anything. I think I learned more working at Bloomberg than I-

did in school. They put me through multi-week courses, learning the markets, the stress of answering questions, talking to analysts and hedge funds and PMs. It got me excited to want to work in the hedge fund industry. And then realizing that I always liked the startup type of atmosphere and investing in early stage startups and just building stuff and kind of found my way to investing in VC. So I would say embracing those tools.

continuous learning obviously is so important. And then I think the most important thing is like networking. I don't think people realize that like, some of the best schools and MBA programs and all of those things, but the best value that you get out of those besides that on your resume is like the network and the alumni network. So going to other schools that aren't as, you know, well established, just means you have to hustle and build your network even more, right? But like me investing early on, on my own,

taught me more about venture wins and losses, investing in venture funds and broadening my network. I think that the power of the network is something that I learned from the founders of the companies I've worked for over the last 15 years.

Mr Matthew Cheung (20:52.094)

And that's something that's never really going to be truly automated because it's a human to human connection with people, which you never know when those connections may... You may talk to them or connect to them or I think always helping people out. You know, when people approach you, if you can, you know, if they're a decent person, if you can help them out, it's always good to do so because karma always does seem to, you know, things come back around again, don't they?

Matt (21:19.288)

That was the biggest thing I think we decided when we were early at WorldQuant was we were going to be the hedge fund that took the calls and meetings with all these vendors. And if we weren't the right user or we weren't going to pay for something, we gave the feedback. And I think that it just kind of opened up that network. And I think social leverage does that really well. It's like if we're an investor and you open up our network for everything, it amazes me every day when a founder is looking for something and I realize, oh, I can connect them to this person and get s-

something done much more quick, it gets a sale potential, it gets a potential investor, but on top of that, it's like, even if we're not an investor, doing the right thing, because if you don't invest in a company, but you help a founder, they're gonna think of you when a friend of theirs is starting a company, right? So like the karma aspect, I'm a big believer in as well. And with LinkedIn at this point, there's such a big opportunity to connect with people, consume content, and I think that there's...

some pretty exciting LinkedIn type competitors that will pop up in the future, right, to find more verifiable contacts and have deeper engagements.

Mr Matthew Cheung (22:29.186)

What's your view on generalism versus specialism? Because you said like when you started at Bloomberg, you're kind of thrown in the deep end, but actually you had training in all these different areas. And then it sounds like as you've then moved into the hedge fund world, you become much more specialized. Whereas probably now you've become generalized again. I would try and kind of guess. But what's your view on that? Because one thing that when I'm mentoring students, when you're going through...

the traditional education system that's unchanged for 200 years, you become more specialist every time you go up from, you know, when you're studying at high school to college to university to grad school, you become more specialist and at the end of it, people think that oh now I've got a masters in finance, I have to go into finance and you, people kind of get brainwashed in a certain way because that's the way the machine spits them out. Whereas like you say, as soon as you start your first job on day one.

everything that you learn really, most of it's going in the bin and you're learning from scratch again, which is when your generalist skills are very powerful and retaining them as your career goes, I think is a really powerful tool. How do you see that?

Matt (23:45.752)

I mean, listen, I think you can always reinvent yourself and every step in your career is to get to the next level, you have to be uncomfortable. Maybe it's five steps forward and you take a step back to kind of move higher and higher up, right? You bet on yourself, you take risks, like it's all important to kind of continue your journey. I think that going deep into a topic that you're interested in is important, right? When I was at Bloomberg, equity derivatives were my thing. I became a specialist there, an advanced specialist.

That is really what I thought I wanted to do. And I thought I was going to leave and be on an equity derivatives trading desk. I ended up going to world quant. And the only thing that mattered was like understanding all of the markets, right? Cause we traded every market and it was important that I understood what swaps and CDS and futures markets did. And I also understood, you know, a bunch of technical analysis and charting. Right. And I quickly learned that world wants to be successful. It was going to be about data and like, as much as I needed to understand the quantitative aspects of things and the programming.

There were people that were 10x smarter than me in those fields. And my value was to think about how can we exponentially grow our data machine. Right. And I think going to third point, it brought me back to we invest in everything. And data was important, but building teams and thinking about like just the management of building a business was, you know, important within a bigger organization. Um, so I think even when you specialize.

there's things that you specialize in that can be leveraged in the next step in your career. So I don't think you're pigeonholed, but I do think that the value for me at Bloomberg when I wanted to be in derivatives is I consumed as much as I could in books and readings on volatility and all these different aspects. And it gave me the confidence to understand that area very well. But I think with continuous learning, you can continue to...

expand to other areas. I haven't lost all of that, but I'm also probably not the one that's going to do evaluations on structured notes anymore. But I do still remember some of the pieces that I could have a conversation about it. I think it's important for young people to, when they find something, to really dive in and really consume as much as they can about it and continue to learn, but also realize that that doesn't mean you can't take the next step and do something different.

Mr Matthew Cheung (26:08.846)

Final question, if I'm a student and I'm interested in startups, and I might think I have an idea and there's various things that, you know, approaches that people need to do with, you know, pitch decks and models and all this stuff. But we always hear that in early stage investing, a lot of it is about the founders and the person and the founding team and as well as the idea and the plan is kind of one thing, but there's a lot around people.

Mr Matthew Cheung (26:38.666)

And therefore, if it's about people, there's this element of communication and soft skills and other things around people as well. Can you just talk about some of the things with you, with your investor hat on, that you look at when you're looking at people in these early stage ideas?

Matt (26:56.056)

Yeah, I mean, listen, I think at the early stage in seed investing, it's definitely all about the people. There's less numbers to analyze, right? You have to believe, is this the person, when things go wrong, that they have the grit to continue to keep going, right? Very few companies that start and continue down doing the same exact things without any curve balls or missteps or pivots, right? Understanding the team dynamic. Is it a founder by themselves? Do they have somebody that they're working with? How long have they known each other? How many founders are there?

Matt (27:27.480)

The more founders there are, the more potential conflicts or just changes in their life that happen over time where it may not work out. And then what's the ultimate goal? I think a lot of times people want to build a venture-backed company but don't realize what the VC investors are ultimately after and what their LPs expect of them. And maybe they want to build a business but it just doesn't have the capability to be as big as a VC needs it to be to make it make sense from an investment perspective. And that's okay.

There's a lot of great businesses that can be built that don't need VC investors. And there's other types of investors that could help them. And the outcome doesn't have to be hundreds of millions of dollars, right? It could be a great company that one day sells for 25 million. And I just think that understanding who you're pitching and what their perspective is and what they're after is just as important as, you know, building your own company. So I think it's educating yourself on who.

who the right people are is to help you, and what you need from them, and then also what are they in it for, so that you're aligned.

Mr Matthew Cheung (28:32.462)

Because Matt, it's been great talking to you. It's been great to hear about the story of your career and also the importance of data automation, how you as a person and a company and a business where you have to work or may work needs to be scalable and how the tools that are out there now, like AI and some of the things we've spoken about, it's gonna help enable that. You know, you enable your own personal productivity. And on the...

all the kind of soft skills and personal skills, the power of the network, the power of networking, meeting people, staying in contact with people because you never know where that may bear fruit.

Matt (29:12.256)

Yeah, I completely agree. It was great speaking with you.

Mr Matthew Cheung (29:15.447)

Yeah, thanks a lot, Matt.

Matt (29:16.868)

Thank you.