My first-ever Medium post is intended to discuss some research I conducted lately. I learnt a lot while working on it and would be glad to share some of that knowledge as well as my key findings and main takeaways
Topic: Because I am graduating in a few months I am more than ever concerned about my future. Venture Capital would be an exciting option (dream?)
Venture Capital is a small buzzy world where open positions are as scarce as coquillette packs were a few weeks ago
To perfect my jump-in strategy I decided to have a close look at current Venture Capitalists’ (VCs) backgrounds, chasing for common patterns
Spoiler: I found some !
Even if you are not interested in that topic you may still find some interesting tools/tips/tricks in what follows! (and average VC’s face computation!)
The first challenge was to gather data about VCs
The best professional information source available is without a doubt LinkedIn so I used it as my main playground
Starting from this list compiled by Serena, I identified ~60 VC funds based and mainly active in France (see the full list at the end of this post):
I scraped (Phantombuster🙏) these funds employees’ profiles, resulting in a raw 1065-entries list encompassing all publicly available LinkedIn data (mainly about work and education history)
After cleaning the data some processing was required, the main challenges were :
- Identifying VCs who belong to the investment teams (and not operating nor support teams
- Computing and categorizing VCs’ work and education history (main challenge was to design an algorithm able to understand and to categorize former jobs)
- Completing the data with key missing variables: age and gender
No manual categorization was done because (i) too long (ii) too painful (iii) even the sharkiest shark would feel weird stalking all these people
Gender refers to female/male and is arbitrary, my Python capabilities are for the moment not sharp enough to design a more inclusive approach
VBA and Excel’s visualization tools got my back for the first two points.
The third one was the most challenging and the most exciting one. I figured a way to estimate VCs’ age and gender automatically using a pre-trained Computer Neuronal Network (Computer Vision x Deep Learning) implemented in Python
Was really fun to do and results look pretty accurate!
I eventually ended up identifying 492 VCs
(VC = investment team + located in France)
Game was on!
Having data is great, leveraging it is better!
So I asked my questions to the dataset and here are the answers:
1. How old are VCs?
Ages range from 21 to 65
Average VC is 38 years old and median VC is 36
Are VCs younger than other finance professionals?
Yes, as in general finance according to INSEE’s data the proportion of 25–49 years old is the same as the 50+ years old
It is not true for VCs: There is 7x more 25–49 years old VCs than 50+ years old ones
2. What does VCs’ age wave looks like?
25% of identified VCs are women
While median Mrs VC is 31 years old, median Mr VC is 38
Even if global gender parity is still a work in progress, figures are better among young VCs (gender parity is even almost reached in the 25–30 years old class)
Is the gender ratio better than in finance as a whole?
No, according to INSEE’s data (via Xerfi 😉). France-wise proportion of women holding executive position (Cadres) in finance-related activities is around 41% (vs 25% here, assuming all investment teams’ members are executives)
Some funds behave better than others regarding parity, with 8 out of 60 achieving a feminization rate in investment teams of 40% or more (some others still have a lonnng way throw)
For further and more precise information on gender diVerCity (diversity x VC), check out SISTA’s amazing work !
3. VCs education history
60% VCs graduated from the same 10 schools, which are (almost) all located in or near Paris
Tier-1 business schools are #1 workforce source for French VC funds, followed by leading universities and engineering schools
All the usual suspects are represented, nothing really fancy there. Some more interesting data show up when digging into details
School diversity in more important for men than for women. +70% Mmes VCs graduated from the previously mentioned schools versus 60% for Messrs. VCs
HEC ranks #1 for almost all age classes (except for 45-50 years old for whose INSEAD takes the lead)
School diversity is less important among VCs below 35, ~70% coming from the same 10 schools (vs <60% among 35+ VCs)
In total our VCs attended +110 different graduate schools (> high school) mainly in France but also in Europe and other parts of the world
4. VCs work history before their current position
No difference is made between full time positions and internships in what follows
Analyzing VCs’ professional experience before Venture is not easy: it represents a total of ~2400 experiences in +1000 different companies
Average VC worked 9.1 years in 5 different positions before joining his/her current role, median VC only 6.9 years in 4 different positions (some “late” jumps in are affecting the distribution/once again internships are included)
️Mrs VC worked on average 5.5 years before jumping in, median is 3.4 years.
️Stats are double as big for Mr VC in line with previously presented age wave (10.7 years average and 9.2 years median with a bigger standard deviation)
I broke down those experiences into 6 categories, to better understand VCs’ background:
- Advisory: 76 experiences
- Corporate: 299 experiences
- Finance (excluding VC-linked finance): 464 experiences
- Startup: 193 experiences
- VC&Co (including incubators, fundraisers and VC-linked finance): 245 experiences
- Others (SMEs, startups I do not know about, “Marine Nationale” and everything else): +1000 experiences
This mapping is arbitrary and only based on my knowledge, I basically sorted the whole list into 5 categories I am confident about and put all the remaining ones in the Others category
Before Venture VCs did many different things, not any category appears as a real VC maker!
Finance is the category most VCs have in common, as nearly 51% had at least one experience in that field
Corporate and Finance seem to be the main identified providers of late jumpers, but it is possible to find VCs with +20 years of experience in any of the categories
Only 15% of the VCs are background pure-players (ie have previous experience in only one of the categories), most of them touched 2 (36%) or 3 (31%) different fields
Have all VCs had an entrepreneurial experience?
31% of them have at least one job title containing “(co-)founder” or “CEO” on LinkedIn
Who are the VC builders (excluding VC funds)?
Recurring names include BNP Paribas, EY, L’Oréal, Lazard, Deloitte, Société Générale, Accenture, Air Liquide, Orange, Natixis, Ardian, Rothschild, BCG, Bain, etc.
Once again, all the usual suspects are represented!
5. What does average VC look like?
I always wanted to try averaging faces; this is my time!
Let me introduce average Mrs and Mr VC:
Pretty, aren’t they ?
They are actually resulting of VCs’ LinkedIn profile pictures averaging!
6. Are all VCs LinkedIn flexers?
Our average-VC has 2715 LinkedIn followers vs 1659 for our median-VC. Dispersion is important regarding VCs’ network size, even after eliminating two outliers (>75k followers) from the data (can you guess who those two French stars are?)
254 out of 492 (52%) VCs have a LinkedIn description.
After running some analysis this is what they are saying the most (word’s size representing its apparition frequency):
Average description is composed of 71.5 words of an average length of 6.3 characters representing, once again on average, a total of 470 signs
When looking at recurring expressions the more common ones include: “demonstrated history working”, “feel free to reach out”, or “strong business development professional”
7. How much does average VC make?
Not a clue!
Conclusion: the Average VC
☑️ He is 38
☑️ He graduated from one of France’s top business/engineering schools
☑️ He worked 9 years in 5 different positions before starting as a VC (including everything from internships to not filling his LinkedIn properly)
☑️ He has at least one experience in Finance, and probably worked in one or two other fields
☑️ He did not IPO a startup he created (if any)
☑️ He has 2715 followers on LinkedIn and a beautiful description
𝗜𝘀 𝘁𝗵𝗮𝘁 𝘆𝗼𝘂? 𝗛𝗼𝘄 𝗺𝗮𝗻𝘆 𝗮𝘃𝗲𝗿𝗮𝗴𝗲-𝗩𝗖 𝗯𝗼𝘅𝗲𝘀 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗶𝗰𝗸?
I have nothing more to conclude on, as all the stats exposed are lacking statistical significance and are only aiming informational purpose
Are these numbers surprising? Not really, aren’t they?
Takeaway: Articles I read on related topics 🔗
- Alexandre Dewez’s Breaking into Venture Capital — European Junior VC Career Paths #JumpingInVC🔜
- Harlem Capital Partners’s Navigating, Entering, and Thriving in VC #JumpingInVC🔜 #diVerCity
- Crunchbase’s Special Report: EU Lags Behind US In VC Gender Equality, Albeit With Signs Of Change #diVerCity
Takeaways: Excel Jutsus I used 🖇️
VC funds list
360 Capital, 50 Partners, Aglaé Ventures, Alven, Aster Capital, astorya.vc, A-Venture, Axeleo, BlackFin Tech, Bpifrance Digital Venture, Breega, Brighteye Ventures, CapDecisif, CapHorn Invest, Cathay Innovation, Creadev, Daphni, Demeter, Educapital, Elaia, Eutopia Team, Fa#, Five Seasons Ventures, French Partners, Frst, Gaia Capital Partners, Hardware Club, Hi inov, HUB612, Idinvest Partnerst, Iris Capital, ISAI, Kerala, Kima, Korelya, KREAXI, NewAlpha, Newfund, Omnes, OneRagtime, Partech, Phitrust, Quantonation, RAISE, Red River West, Ring, Serena, Seventure, SGPA, Side Capital, Starchain, Starquest Capital, Supernova, techmind, Truffle, Ventech, XAnge