How valuable is a data science role at a VC?
For context, currently sitting on multiple offers for data science "quantamental" positions at name brand L/S pod shops.
Was just approached for a data science position at what I can only assume is a very strong VC platform (Sequioa / FF / etc.) but have zero clue how data science at these firms are perceived. Seems like the role revolves around utilizing alt data sets to assist in due diligence and portfolio company support/monitoring.
There are several DS roles at hedgefunds (non quant) that have PnL and discretionary bonus structure where the DS role has actual input on the investment process but the current state of DS at VC seems much more early stage and was hoping to get input on whether or not these types of roles are worth pursuing.
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Worked at VC that built it's own data eng org in-house. The budget for data acquisition in VC is much smaller than HFs due to the fund size and monetization of the data insight. i.e. knowing about a high quality company is the easy part, getting an allocation in their next round is very difficult. Data doesn't necessarily help you with that.
The flipside is that you're likely the only really technical person on the team depending on the stage so you may be brought in to do more technical diligence or help portcos think about data architecture.
I was good friends with the CTO of our VC that ran the data eng org and he eventually left to build his own company since it was always viewed as a cost center that helped automate parts of their investment process but not drive new opportunities.
Thanks for the colour here. The fund in question has a strong history of top decile returns and offers carry as part of the compensation package. Have you found to be typical?
Carry is common, but since this is VC don't expect to get paid out on any of it for a while (5+ yrs).
I'd ask them what they expect to gain from this data and how it factors into their investment decision process. I'd note that since you're not part of the investment decision making process you'll have a more "chill" job in that you'll have a lot more autonomy to learn about alt data sets and best practices. It's not a bad lifestyle but it's not investing.
VCs tend to have much lower budgets than PE firms and even hedge funds for anything related to diligence or data.
I would go for a data science role for any financial services role, including even at a JPM/MS/GS or at a merchant bank / large asset manager / large secondaries fund before you go with a VC.
It's mostly monitoring people and companies which the above poster rightfully stated is not very valuable unless the investment team can actually capitalize on the data by making an investment.
DS roles at VC firms are garbage. Unlike public markets where alt data is rich and high signal, startups have little if any observable data until they're very large, at which point they're so obvious there's no point in using alt data to find them. I have yet to see any "data driven" VC firms actually perform well. Also, any non-investing role is viewed as a cost center with limited if any career advancement, small economics, and no real power within a VC firm.
Back office, under valued and under supported
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