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Why VCs should hire an engineer instead of another investment professional
DDVC #34: Where venture capital and data intersect. Every week.
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.
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84% of VCs want to ramp up their resources and become more data-driven, yet less than 1% of firms do have dedicated engineers working on such initiatives. This is the result of two recent polls and research we have conducted as part of the upcoming Data-driven VC Landscape 2023.
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What explains the gap between ambitions and reality?
Over the years, I’ve talked to hundreds of aspiring data-driven VCs and repeatedly heard the same reasons preventing them from achieving their goals:
No consensus among Partners to allocate the budget
Too busy with day-to-day to focus on new initiatives
Stuck in “buy versus build”, tried external off-the-shelf solutions and also worked with freelancers, but not happy with either outcome
Don’t know where to start, whom to hire etc.
How to close the gap?
Looking at 1. and 2. above (the majority of VCs), I’m convinced that results will speak louder than words, meaning that data-driven VCs with dedicated initiatives will outperform the market in the long run, eventually convincing skeptical Partners and investment professionals who are not willing to sharpen the saw (=step back, level up their tool stack and get more done with less). With sufficient sample size and control variables, we’ll hopefully be able to prove causality outside-in. The first episode of the upcoming Data-driven VC Landscape will be another building block in this picture.
Inside-out, on the other side, will be even more explicit as data-driven VCs themselves can easily attribute which deals were sourced manually versus through data-driven initiatives. This is also true for solutions purpose-built across the VC value chain. At Earlybird, for example, we measure efficiency, effectiveness and inclusiveness across several dimensions, as described in the “How to measure productivity and identify potential for improvement” episode.
Unsurprisingly, many of the leading data-driven VCs don’t speak about their initiatives, trying to keep the positive impact for themselves. Only a few openly speak about it, which of course reduces the incentives for other firms to do the same. Intentionally, these silent but highly active data-driven VC firms try to leave their competitors behind. There are pros and cons, just like closed-source software versus open-source software. I personally believe a lot in the power of community.
Looking at 3. and 4. above (the minority of VCs), I see that the respective VCs have the conviction and budget but are frustrated as there is no blueprint out there. Therefore, firms typically start with the lowest cost (to reduce downside) and test off-the-shelf solutions, in most cases for sourcing and lead gen. Clearly, this is better than doing nothing, yet if everyone uses the same tools, it won't be easy to create alpha long-term. Moreover, customization needs oftentimes need to be left behind.
As a natural next step, most VCs take the next “least downside” step by engaging a freelancer to stitch together some web crawlers, APIs and a simple database to meet the customization demands, getting one step closer to uniqueness and the ability to generate alpha.
Some months into the project, I can ensure you that from my own experience as well as the experiences of many befriended VCs, you will get frustrated. Most freelancers lack an understanding of the VC job and have no incentive to take ownership and learn the basics. Therefore, the project either gradually pulls someone from the investment team in (👋🏻 my friends) or gets killed completely.
Why hiring an engineer will be the smartest move you can take as a longterm-oriented VC
General Partners (GPs) in VC firms have the incentive to reduce costs to eventually increase their profits from the management fee. When making budget decisions, they need to consider short- and long-term perspectives. One frequently used anchor for input/output is the hire of another investment professional. In this scenario, what are the costs and benefits of hiring your first engineer compared to hiring another investment professional?
Short-term oriented Partners will rather go for an additional investor as the respective results (new deals sourced, customer or investor intros done, event participation, etc.) will be more immediate and measurable. Long-term oriented Partners, however, will see the rather intangible benefits of hiring their first engineer as her efforts will allow the existing investors to significantly scale their work over time. It’s like sharpening the saw.
Tool stacks of VC firms without dedicated engineers are fragmented and vary a lot across individuals. While some investors are keen to try out new tools, the majority are stuck in their comfort zone, not willing to change their workflows whatsoever. Hereof, productivity across the team highly varies.
Your first engineering hire can change this and lift everyone to the top levels by understanding the status quo across the team, identifying the best tools for each workflow and then starting to outline how internal, custom tools can complement off-the-shelf solutions. Certainly, the right full-time engineer (versus freelancer) will go the extra mile to understand the VC basics, take on full ownership and start creating a useful data-driven tech stack.
Lessons learned from building an engineering team within a VC firm
The right background: Given my own background, I initially completely overestimated the importance of Data Science and underestimated the importance of (full stack) engineering. Today, I recommend starting with an engineer as close to a Swiss army knife as possible. Experience in large-scale distributed systems and web scraping is as important as knowledge about modern databases, API integrations and data pipelines. Don’t start with too deep expertise. PhDs are fine, but probably not what you need in the beginning. Hands-on development experience across several large-scale projects outweighs theoretical understanding of ML models.
The right seniority: Following the argumentation with respect to 3. and 4. above, many VCs will continue to limit their downside (costs) and go for too junior hires. Today, I recommend hiring someone with 5-10 years of relevant experience to lead the initiative and potentially hire further engineers in the future. Less experience might lead to an increasing amount of trial and error. Hereof, not only the opportunity costs of delivering a great solution earlier but also the actual costs accrued for a specific outcome will increase. More experience on the other hand might lead to friction as it’s extremely challenging for an engineer to find her way in a fast-paced VC environment. I’ve seen few experienced engineers willing to put in the required effort to understand the VC basics, workflows and lingo, and to deal with such a high-pressure environment in rather advanced phases of their life.
The right mindset: Unfortunately, many engineers within VC firms at some point of the project want to become an investor themselves. Either they knew it from the very beginning and only took the engineering/data role as an entry point to becoming a VC (while hiding these ambitions in the first place) or they got increasingly more excited about the VC job than their engineering responsibilities when learning all about the investor profession. It’s a fine line but as soon as engineers ask to get more exposure to founders, participate in deal meetings and overall want to be more involved in the investment business, it’s a signal that their focus shifts. They will spend less time on building the data-driven infrastructure and eventually their value will diminish. I recommend looking for someone who is an engineer by heart and wants to apply her knowledge in a new domain. The more they know about VC in their job interview, the more sensitive I become.
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If you have any suggestions, want me to feature an article, research, your tech stack or list a job, hit me up! I would love to include it in my next edition😎