Neil Patel

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In a recent episode of The Dealmakers’ Podcast, Alexander Izydorczyk shared his remarkable journey from an academic upbringing in Winnipeg, Canada, to becoming a pioneering force in the data science field.

His company, Cybersyn, has successfully created a data-rich ecosystem and raised $62.5M to empower governments, individuals, and businesses. The venture has attracted funding from top-tier investors like Sequoia Capital, Snowflake, and Coatue.

In this episode, you will learn:

  • Growing up in an environment of academia and values of excellence and healthy competition
  • Delving into the cross-disciplinary field of data science
  • Recognizing the potential of data to drive powerful insights and its capacity to revolutionize industries, echoing the inspiration from the movie “Moneyball.”
  • Working at Coatue, a hedge fund, transforming from a research analyst to a driving force behind Coatue’s data science division.
  • Working with alternative datasets and technology positioned Coatue at the forefront of data-driven investment.
  • Pioneering data-driven innovations by collaborating with Snowflake, a data warehousing platform, and eventually, setting up Cybersyn—a data-as-a-service platform
  • Democratizing access to high-quality data and showcasing the power of collaboration in bringing about meaningful change.


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About Alexander Izydorczyk:

Alex Izydorczyk is a former Partner and Head of Data Science at Coatue Management. Alex built and managed the data science effort at Coatue from 2015 through 2021. He oversaw the technical and commercial vision behind incorporating data in public and private investment processes.

Alex is also the founder of Cybersyn which empowers businesses, individuals, and governments and their decision-making processes with robust databases.

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Connect with Alexander Izydorczyk:

Read the Full Transcription of the Interview:

Alejandro Cremades: Already hello everyone and welcome to the dealmakerr show. So today. We have an exciting founder because a founder that you know basically got it to data science then you know hedge funds and now he is doing something really interesting and quite a serious era. He’s done so his journey quite inspiring. So without further ado. Let’s welcome. Our guest today Alex is isoring welcome to the show. So originally you were born in Canada you know to immigrant parentsent so get us I walk through memory lane. How was life growing up there.

Alexander Izydorczyk: Thank you, It’s great to be here.

Alexander Izydorczyk: Yeah, it was great I mean I grew up in Winnipeg canada so think ah the Kansas of canada central canada both my parents were university professors I’m a triplet actually so kind of grew up in a very academic and healthy competitive environment. Um, that was also relatively quiet so moving to the East Coast was a big change.

Alejandro Cremades: So they and tell us about moving to the East Coast because obviously the idea of wton came knocking mean that that’s a pretty incredible achievement you know from in the middle of nowhere in canada all of a sudden you landing one of the best universities in the world I’m sure that your parents. Being immigrants. You know that were always about giving you guys a better tomorrow. We’re quite proud.

Alexander Izydorczyk: Yeah, for sure and I think you know again, they’re both university professors. So I think that in some ways you know they were very supportive of looking you know around the world for the best universities that we could go to? um you know, but it’s one of those things where you know in Canada universities are free or merely free. Um, and and that’s not the case in America and so my perspective was look if I get into one of these fantastic schools. Um, then it’s probably worth going. But if I get into an average school. It probably makes more sense to stay in Canada and I was sort of fortunate enough to get into wharton. Really liked the combination of sort of stretching myself to do business I’d been programming since I was like 12 so I sort of knew that computer science coding was an option for me. Um, but I always felt that like mixing. You know, 2 different skillets together make sense if you’re not in the top point one percent of the field right? So I knew I wasn’t going to be. You know the next best coder on the planet and so it felt like there’s very few people who have something cross-disciplinary and that’s kind of what pushed me. To to go to Wharton rather than going to an engineering school and that theme I think has played out throughout my life in other ways.

Alejandro Cremades: So then tell us about what was it like to be there in Wharton because I mean there you have an amazing community full of opportunities. You know, a lot of people go into consulting into many like launching their startup like for example, the Warwi Parker guys or like in your case data science and you know basically hedge funds.

Alexander Izydorczyk: Yeah, yeah, so you know it was. It was an awesome community. It was ah like I would warn it was very preprofessional. Um, definitely is somebody coming in from kada no idea that sort of people come in and they’re like ready to work in investment banking or consulting. Um, you know in freshman year. So very kind of intense and preprofessional. Um I would say that you know at the time data science was still pretty early like think back to 20112012 I think tech and specifically data science was less of a magnet for Wharton students just yet. But I was very lucky that the statistics department at Wharton was in the wharton school and so you can major in something very technical statistics and still do all the normal finance management type courses and then sort of interview for a mix of roles and so you know. That’s where I found my home and I think one of the underappreciated aspects of Wharton was the the graduate programs I made a lot of friends with professors graduate students in Wharton um, you know I had a lot of Ra jobs and some of the best people I met were actually grad students in ph d programs.

Alejandro Cremades: Are.

Alexander Izydorczyk: While I was at Wharton and I found that sort of very intellectually, um, stimulating and challenging and kind of again. Interesting cross-disciplinary mix of the quantitative and sort of the applied.

Alejandro Cremades: Um, so how do you get into the whole thing around Data science.

Alexander Izydorczyk: so yeah so I mean again I I kind of knew I wanted to do something technical on you I liked to code but I think the thing that really pushed me over the edge was moneyball. The movie about Billy Bean and the Oakland a’s came out in freshman year. And it was all about this kind of concept of using statistics to turn a low-budget. Um, you know a baseball team into a high performing team by you know, buying underappreciated players that would perform better um than their prices indicated. And I sort of watched that in freshman year and I still remember going to the movie theater and watching that and being like wow. That’s so cool. That’s so interesting. That’s what I want to do and I didn’t really care about baseball at all. Um, but just the concepts um seemed to make a lot of sense to me and it seemed to vibe very closely with sort of statistics. And what was sort of becoming data science. So that that that’s what got me interested in in in the field.

Alejandro Cremades: And what about hedge funds. You know how do you landing Coach you How what was that process like.

Alexander Izydorczyk: Yeah, so so I was fortunate enough to be introduced to you know a senior partner at co 2 so. There was a little bit of dumb luck involved candidly in getting introduced to kotu specifically. But what I did know is I did know that I wanted to work on the buy side. Um, and then there was this question of like okay do you go work at a 2 sigma or an aqr or sort of a quant firm or do you go work at a discretionary firm like Kotu and try to sort of do technical statistical work at a traditionally discretionary non-technical firm. And you know I sort of came to the conclusion that again if I go to two sigma. There’s going to be you know 20 other graduates just like me that essentially have the same skill set and if I go to a place like cotu where they’re not doing data science yet you know again I I can build a cross-disciplinary skill set and be 1 of the first. So I just thought. Strategically it made more sense to go to to co 2 obviously coach. You had a good brand at the time and you know Philippe laantt the founder was in hedge fund circles very famous at the time already and very well respected. Um, so I you know it was. It was an obvious choice in that sense. Um I think that. When you go through Wharton again I mentioned it’s very preprofessional. You know I think when you go into Wharton you kind of see all of finances this kind of blur right? like investment banking sales and trading buy side like honestly most freshmen at wharton don’t know the difference and so as you kind of go through the years you realize

Alexander Izydorczyk: You know what is a sales job at the at the highest level and what is a a betting job or a intellectually kind of like ah asset allocation job and it became very clear to me that I found intellectually the asset allocation space far more Interesting. So at that point I knew that the buy side was the place for me and then you know Cotu came along.

Alejandro Cremades: Um, so then in this case, you know I’d go you there you know like you literally started on this you know journey on on on pushing the data signs and then all the Sunday’s like 40 people. You know you have you know in the in the team. So.

Alexander Izydorczyk: Yeah, so so you know it obviously wasn’t planned right? You don’t hire so a 20 year old from college and you know impend to make them the head of Datata Science I think what happened was you know I joined as a research analyst um, and you know I was working for you know investment analyst that covered internet. Names and you know we quickly discovered that there was you know data-driven approaches to you know, generating investment alpha and these things you know at first they became like clever tricks right? It became like hey you know if you go to, you could sort of see. Um, what aws server was serving that Netflix Page so if you visited that Netflix page 20 times you could see how many servers they had spun up and so you could benchmark sort of the relative success of different pieces of original content on Netflix and so it started off with tricks like that. And then it graduated into you know, procuring and buying large datasets which became known as alternative datasets think credit card data click stream data point of sale data anything that could measure what businesses and consumers were doing in real- time and that moved faster than quarterly reports. Government reports and you know these things required progressively more and more technical skill and more and more engineering skill and so it started off with hey this is really interesting. You know, hire yourself an engineer to help you process this data or hire another person to help you make more estimates.

Alexander Izydorczyk: And sort of before you knew it. You know there was like 5 of us working on this problem space and so so it organically grew and even at that point you know I was young I was 23 24 um so there was always this question of like oh do we need to hire a Vp of data science from Google right to run the Kochu Data Science team and. I think candidly what we found was although those people certainly had more experience than me in running data science organizations. They had much less experience. Um in you know, basic financial investment concepts right? like they would interview and you’d ask like hey what did you think of earnings this afternoon or like. You. You know how would you model? Um, you know Mcdonald’s revenue versus Chipotle’s revenue why are those different financial concepts Mcdonald’s of course is a franchise business and you know most people in Data Science don’t have that that skill set or that knowledge at all right. And so it sort of became this alternative data field and it’s called alternative data because it’s you know, alternative to traditional quantitative ah market data. Um like price data volume data. So on this field was pretty new so there was nobody with. 30 years of experience in this field and so at that point you know I’d already been running the team effectively for 3 years and so you know I had as much experience as anybody in that field and so they just let me kept growing with it and gave me a lot of leash um to to keep building. It.

Alexander Izydorczyk: Um, which I’m obviously very grateful for and you know goes to show that some of these hedge funds that operate in very lean models are great places for young people. Ah to to start their careers because if you’re successful. They sort of give you a lot of rope. Um, and if you’re not successful. You know it’s still a fantastic brand. You still get a fantastic training. Um, and and so on and so you know the the founder of Cotu always gave this kind of advice to young people. Um, he has a ah interview and and I distinctly remember this because I watched it before I joined Kutu and he says you know. When you’re starting your career you want to do 1 thing conventional and 1 thing unconventional and so the conventional thing might be. You know, go work at Goldman or go work at a traditional good brand where you’re going to get good training and then sort of have more doors open to you as a result of that training. And then do sort of 1 unconventional thing that makes me different and you know at code 2 I got the best of both worlds where I got to work on the buyside you know sterling brand that has only gotten better as the firm has has grown and grown successfully and at the same time I got to do something different and unconventional in. You know, not just being an investment analyst but really focusing on this data science angle which was new. Um, so you know I feel like I’ve lived that and coach who gave me the opportunity to do that.

Alejandro Cremades: Um, and how did you? um I mean how did the idea of cybersane. You know come knocking. How was that process you know from ideation all the way to launch.

Alexander Izydorczyk: Yeah, great question. Well so it was ah it was a complicated it complicated road. So first of all I would say that you know while I was at code 2 we were buying a lot of external data and so we were working with traditional data providers. Um to acquire data and the question was always like where do we get. The most valuable insights and you know a lot of these data vendors are frankly legacy businesses. They’ve had pseudo-monopolies for 20 to 30 years they you know they’ve had less innovation to innovate. Um, they’ve been delivering the data the same way for the longest time. And at the end of the day hedge funds are a little bit of a niche space compared to other data buyers and so therefore you know we’re not always the most important client even if individually we’re paying millions of dollars for the data so I was always a little bit frustrated with their data vendors I always thought they could do better. So I think and and and not just do better in terms of the types of data they provide but also both the way they provide us that data the technology the metadata, the reliability just all these things where I always felt you know there was a better way to be a data provider. That could take advantage of sort of more modern technology and frankly be a pleasure to work with so that that was my first inkling but I never you know I wasn’t intending to leave co 2 at all. Um, you know the other thing that happened is we started using snowflake and snowflake was really a game changer for us.

Alexander Izydorczyk: Um, it, you know was all you know besides being a data processing data Warehousing tool. They launched this concept of the data marketplace and that data marketplace was super useful in reducing the amount of etl time we needed So The amount of ingestion engineering we needed to bring data in from wherever it was coming From. Emails excel Files sftdps S 3 buckets and snowflake reduced that because suddenly data vendors could be using snowflake and they could just share that table with you and so I thought Wow this is going to. You know this is so helpful. This changes a lot if the snowflake marketplace takes off like this this really makes my life easier. And probably makes a lot of people’s lives easier. You know you could see the data Dictionaries. You didn’t have to talk to salespeople. There’s all these great things and and I really got behind that and so not only were we a customer but we eventually became an investor and I spent a lot of time chasing down the snowflake Team I.

Alejandro Cremades: Are.

Alexander Izydorczyk: Spoke at their all handss I got to know Frank and some of the executives like christian@snowflake and built a relationship and so you know when i when i eventually decided to take a break i was turning thirty I’d worked at co 2 so far the entire career I really felt you know. There’s points in life where you need to evaluate and say okay I’m working a lot. Maybe I need to take a break and sort of focus on sort of time for myself for for mental health for you know, just to decide like look what does the rest of my career look like at that point it was already a partner at Kutu. I was financially successful I felt you know like I I needed some time to work on myself personally and you know in that time snowflake sort of reached out to me and said hey you know you should come work at snowflake when you’re ready to get back in the game and. I was very interested I thought extremely highly of the snowflake management team but at the same time you know I wasn’t a database developer I was a user of databases and a user of database contents. Um, and I also didn’t want to be a salesperson or you know a sort of thought leader alone I still wanted to build things. And so you know 1 thing led to another and I and sort of made the pitch of like hey look why don’t we build a content layer a new data as a service business for the snowflake marketplace sort of if snowflake is the netflix.

Alexander Izydorczyk: Then cybersyn can be the sort of original content layer on that Netflix in the data marketplace and I was fortunate that they really liked the idea and you know one of the challenges with vita as a service businesses. Um, and you know Oren Hoffman at safegraph talks about this a lot um is. The cap x. The upfront cost is just very very high. Um and so I mean at a capital partner that would be willing to sort of fund us in a nontraditional way. Um, and when I say nontraditional I just meant I would have to raise a lot of money upfront. Um, and so snowflake obviously was a partner that was. You know position to do that from a business perspective and has strategic reason to do that and so they they offered and I thought look this is ah this is a great match. It’s strategically aligned. They have the capital resources to execute on something that’s going to be very expensive. Um, they could be good partners. And so I went back to cotu and sort of um said look I’m I’m going to do this thing with snowflake. Um, but I would love you know some independent governance and at this point I had ah had a relationship with cotu since I was essentially nineteen or or 2020 I think years old. So. I felt it would be great to have cotu involved as well. Um, and so I was fortunate that Thomas Laont the coach who’s co-founder offered to be on our board and invest. Um, and then we also included sequoia which is a firm I’ve gotten to know as a competitor when I was at cotu but also have a huge amount of respect for and they’ve been.

Alexander Izydorczyk: Very helpful in a unique and different way as well. So that’s what put the round together and that was in August of 22 and sort of spent the first couple months you know working on data deals and acquiring data and then this year really started putting the team together and launching data products.

Alejandro Cremades: Now for the people that are listening to really get it. What ended up being the business model of cybers and how do you guys make money.

Alexander Izydorczyk: Yeah, So so the the simplest way to think about it is we sell data as opposed to selling Software. We sell data and you know what that means is becomes more nuanced so you know we acquire large raw data datasets that come from. Everything from Retailers. Banks Api Gateways Apps and these are generally large raw dumps of data some of them come from data vendors some of them come from companies that wouldn’t consider themselves data vendors but have some asset they would like to Monetize. We then curate that Data. We Clean it Up. We merge it we join it. We make it usable for Market Intelligence purposes. Um, and we make that clean table available on Snowflake Marketplace and so if you’re a data scientist or a data engineer or a business analyst or a market or competitive Intelligence Analyst. You can then go to Snowflake Marketplace find the datasets you need and buy those datasets and pay for them on an annual or monthly basis like a subscription. So. Our product is fundamentally rows and columns effectively right? we are. We’re selling that data. Um, now. It’s derived. In the sense that we do a bunch of the cleaning. We do a bunch of the data engineering and modeling work to make that data actually solve business use cases and we happen to focus on what I call the Consumer economy. Um, which is um, you know where consumers are spending.

Alexander Izydorczyk: Money and time in aggregate. So really trying to understand sort of what the economy is doing I mean the way I pitch it to people is if if we had access to every corporate database in the United States on an ongoing real-time basis. We could model us gdp. On a minute by minute basis now clearly that’s an exaggeration that’s not possible, but the question is how closely can you approximate that today. The us Census Bureau you know reports gdp on a quarterly basis and reports inflation and unemployment on a monthly basis and so if you think about. Business decision making or more importantly, policy decision-ma how much better could we govern our businesses and govern our economy if we had more up-to-date more real-time data. It should be possible and it certainly was possible to make money that way at co 2 so why not make this concept available to sort of everybody else. Um, you know, growing up I loved simcity like Sim City four was my favorite game of all time and I always felt that you know what mayors and governors do is they just play simcity. It’s just real life. And you know obviously as I got older I discovered that that’s actually not what mayors and governors do but the question is why not right? if you were running New York city why wouldn’t you want a real time view of what the economy was doing and how your actions were shaping.

Alexander Izydorczyk: That economy it it would make for a better world and frankly it would make for more profitable, more efficient and better businesses and so that’s the passion or that’s the vision I’m pursuing.

Alejandro Cremades: So let’s talk about that for 1 minute um if you were to go to sleep tonight and you wake up in a world where the vision of cybers and is fully realized what does that world look like.

Alexander Izydorczyk: It looks like you know I think that a great there’s a lot of things that change but let let me just give 1 specific example. We don’t we’re able to manage our economy in a datarive. Um, empirical way. And so we don’t have debates on the news on is inflation transitory we don’t you know wait for monthly published reports and the market jumps on those monthly published reports instead you know the fed and. The economic regulatory bodies can make decisions and react extremely quickly to what is going on in the economy and sort of demonstrates to constituents that they have a handle on things so when covid nineteen happens. We have a clear sense of what’s going on to the economy when there’s proposals and bills to you know so increase. Um economic stimulation stimulation. So helicopter money. There’s really clear facts about where consumer savings balances stand. And whether we need more stimulus or not and we can still have a political debate about it. But the facts are very clear and transparent about what’s actually going on as opposed to everybody disagreeing on you know is this even true or not is there too much stimulus in the economy.

Alexander Izydorczyk: Is inflation. Transitory is not is inflation going up or down right and having to wait for quarterly or monthly reports to to sort of make those decisions right? and so you could imagine a world where the federal funds rate is changed not you know once a month or every time there’s some there’s a you know fomc meeting. But it’s changed by a machine on a daily or weekly basis to best manage the economy and further smooth out the the credit cycle I mean that world could exist and it would do a lot to smooth you know consumption and ultimately achieve the goal of stable prices in full. Full employment.

Alejandro Cremades: So then obviously here we’re talking about the future I want to talk about the past but doing so with a lens of reflection. Let’s say I was to bring you back in time and I was to bring you back to the moment where you were maybe in Wharton and still figuring out what will be next for you and let’s say you had the opportunity of whispering. That younger Alex and you were able to give that younger Alex one piece of advice before launching a business both that pandy even when you don’t know.

Alexander Izydorczyk: So I think the the number 1 thing that I’ve benefited from is you know, sticking with so as I said before I would echo philippe’s advice on do something conventional and unconventional at the same time. So go work at a good brand where you’ll get good training and then you know I think the other thing is once you’re in a position where you have some rope or some you have people above you that support you or want to mentor you or believe in you or give you some opportunity. You have to stick with it for a long time I mean I was at cotu for you know six and a half years and if you count the internship. It’s like more like 8 years and I think that it would have been very difficult to have the experiences and the opportunities. That I have now the relationship with snowflake. For example, if I had like stayed at kotu for 2 years then go worked at some other hedge fund for another two years then like if I jumped around I feel like although I might know the same amount of things I wouldn’t have taken. Process kind of ends to end and so the biggest piece of advice is when things you know, get difficult. Um you know and and there were times at co 2 where things got difficult for sure. There was ups and downs managing money is is very difficult. Um I would say you have to stick through it.

Alexander Izydorczyk: Um, and you know I like to say nobody really accomplishes anything at a job until they’ve been there for at least three or four years I mean anything less than three years there’s no way that somebody would have actually you know other than maybe sales I think there’s no way to actually have built something meaningful. And so you know in in in that sense I think people maybe underestimate the value of grinding through. Um, what might be difficult in the short term in order to actually build something so when you leave that place. Um. You know you know that there’s sort of a continuation you had sort of a lasting impact and obviously you know it’s easy for me to say because I was very fortunate to land at Kutu where you know the people I worked for ended up being huge champions of my effort and obviously I like to think that my performance had something to do with that. But it’s important to recognize that you know you need to be a good surfer but when you find a good wave. You have to sort of stay on that wave.

Alejandro Cremades: So Alex for the people that are listening that will love to reach out and say hi. What is the best way for them to do so.

Alexander Izydorczyk: Yeah, so find me on Twitter um, I’m Alex Izzy on Twitter um, so feel free to reach out my Dms are open or feel free to email me at Ai at my initials are pretty fortunate in today’s Ai world.

Alejandro Cremades: I’m amazing. Well hey Alex thank you so much for being on the dealmaker show. It has been an honor to have you with us today.

Alexander Izydorczyk: Great. Thank you so much I Really enjoyed this.


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