I'm sort of a sell-side quant (I do AI research). I've been thinking about jumping to buyside a quant shop like many of my seniors have but I recently came across the fact that many PE funds like BX are hiring lots of data scientists.
There was a time when I tried to break into MBB consulting for future exit ops to PE. And I regularly think about using DS techniques to find and aid investment decisions. So this is really interesting me.
Do you think this is just a fad? What's the compensation like? Is DS more of a MO role or can it directly be linked to investment decisions?
Data science is good
Care to elaborate?
I think it's the future as the industry matures & informational edge becomes more important.
I'd go to a quant shop over DS at a PE firm especially if you're doing AI research. You'll probably get paid more.
What's the TC like for DS at your fund? How about growth and promotion opportunities? Tbh, both fields are pretty interesting to me.
Total compensation? I have no idea, but I imagine low 6 figures ($200k)
I alluded to this in another post but this is basically the future of industry. You can run dozens of playbooks and eke out insane efficiencies to the tune of 5%+ IRR in modest cases. This is especially true when working with legacy industries like Industrials or Healthcare that aren't familiar with data science optimizations. There are funds being built around data science strategies right now. It is the way to win in the next decade. The problem is that people like Blackstone still treat their data science guys as second class citizens because they don't really get what's going on and how critical it is to have management buy in and availability of key data.
The other thing people don't realize is that there's a compounding effect. A data science team will receive company data (let's say, a Salesforce CRM and Outlook data). They will parse through Outlook metadata to figure out XYZ. Then, say the firm passes on the opportunity and there is a data destruction request. The data science team has to destroy the core data but does not have to destroy the metadata. So all the juicy learnings, they get to keep, and train their algorithm on, which only becomes more powerful in the future.
OP, don't waste your time on garbage opportunities. You have probably the single most important skillset in private equity in the 21st century.
-- Meathead guy who is bad with computers
What's the pay though?
I understand that DS is important but is it that future PE founders and managers going to come from DS backgrounds important? Or this will significantly reduce cost for us important?
I'll end up applying to both PE DS positions & Quant Research positions at systematic shops since both are interesting. But I'm still curious.
There are already funds popping up where the key man founder is not a deal guy, but more on the data side. You are never going to found a firm as a data scientist; you are going to found a firm as a manager who understands the data play and can get a deal guy to work together with.
Don't worry about the pay. Both jobs will fuck you up with cash. My guess is quant moreso at first but you find the right role in PE and you're one of those guys who ushers in a new way of winning in private markets investing which is waaaaaay more unique and lucrative.
Which funds are you referring to?
I have heard of data science being used in a couple of ways in private equity / venture capital.
1. Sourcing. Using various data sources to identify potential targets for the sourcing team to approach.
2. Investment selection. Using data to provide input into the decision on which asset to buy.
Companies like Two Sigma come to mind as understanding the value of data science and how it can be applied to private markets. From what I understand they have one or more PE focused strategies where data science plays a large role.
Have seen more and more shops make data science a bigger part of their sourcing strategy and very few use it to help drive investment analysis. Personally, I don’t really see data being as big of a disruptor in the PE space as compared to the public markets. Data is so fragmented and unstructured to the point that it’s very hard to derive much value other than with sourcing. At the end of the day, PE is still very relationship driven, as that’s how deals are closed. Having algorithms and automated programs to help identify companies that qualify as viable candidates for acquisition are one thing, but there’s a reason many PE & growth equity firms still employ “smile and dial” sourcing analysts. You can’t really quantify or replicate a Partner’s pre-existing relationship with a CEO of a potential target portfolio company that helps him close the deal because they were squash buddies together back i n college. Computers can help you find deals but not close them. Even if you are sourcing using a computer automated web crawler, finding non-competitive processes is few and far between. There really are no proprietary deals. The closest thing you’ll find are firms operating in the lower end of the MM hounding for niche opportunities.
Any insight into how DS is utilized in sourcing? where are funds get the data from in the first place?
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