Playfair’s Hiring Process Explained (2024)
NB. This is an edited and updated version of the original blog post that Joe Thornton wrote in 2022 for our analyst hiring process that year. The core thesis behind our hiring process has not changed. However, we have slightly amended the steps of the process and wanted to clearly reflect those changes. Please excuse any strange phrasing that has come from editing an article originally written by Joe but edited by multiple people on the same team since!
Playfair’s Hiring Process Explained
Shortly after joining Playfair seven years ago, I wrote a blog post directed at startup founders in which I advised:
- Recruiting is by far one of the most important aspects of building a successful business;
- Without assembling the right team, your startup has virtually no chance of success;
- A massive effort is required to build your team; effort that you personally must carry out and which must be prioritised above almost all other activities.
Luckily, I followed my own advice (for once) as I set out to build our Fund 2 team four years ago. At the time, I dedicated an entire six months to doing nothing else but finding and hiring Chris and then Henrik. Once they came on board, we continued to over-index on hiring in order to assemble our top performing VC team (not just my opinion, we’ve got the numbers to prove it!) with incredibly complementary skill-sets.
As we prepare for the launch of our third fund, I’ve been constantly berating my team with my view that the recruitment process we’re running this summer, to find our next two colleagues, is the highest leverage thing any of us will do over the life of the fund.
Here’s why.
What’s the Point of a Recruitment Process?
It may seem like an obvious question, but we’re into first principles thinking here at Playfair. The first and obvious reason for a recruitment process is that demand will massively outstrip supply for our two open roles. I’m predicting we’ll receive over 1,000 applications, and so we’ll need to figure out a way to pick just two of them.
One option would be to just hire the first two applicants or, once the deadline for applications has passed, to hire two at random. Crazy to consider at first, but random selection has its merits. For one, it wouldn’t be much of a resource drain on the team. Secondly, it would do away with the biassed reasoning each of us interviewers are sure to employ when assessing candidates at interview stage.
The problem with random selection, however, is that it’s a suboptimal strategy when we consider the outcome we’re looking for from the process: to hire the two people who will perform best in the role and as members of the Payfair team.
You see, no two people are alike; and neither will any two people’s performances in a complex role such as that of a VC analyst be alike. Since we believe we’re on an important mission here at Playfair (to efficiently allocate capital to the entrepreneurs who will meaningfully advance humanity by building useful things), our aim is to maximise our success, and to do this we need extremely high performers on our team.
Fine, but how can we know which two of our ~1,000 applicants will perform best in the role? Well, we can’t. Not definitively anyway. But we can definitely do a lot better than random selection. How? First by understanding, from both our own personal experiences and the scientific literature, that certain human attributes are very good (albeit imperfect) predictors of success in a role like the one we’re hiring for (that is, a knowledge role). Second, by understanding that certain assessment methods can be used to (again, albeit imperfectly) predict which candidates possess those specific attributes, and thus are likely to perform best amongst all applicants in the role.
This is why we run a recruitment process at Playfair: to attempt to predict which of the candidates will perform best in the role.
Now let’s discuss what those attributes are and how we structure our recruitment process to find the candidates who possess them. But first, an important note on a unique aspect of life as an investor at Playfair.
Straight in at the Deep End
Fede, Chris, Henrik and I strongly believe that the only way for junior VCs to experience a steep learning curve is to learn by doing and learn by failing. It’s how Fede learned his craft as an angel. It’s how I learned mine when I was the only investor on the team for a while. Chris’ first job in VC was as Playfair’s managing partner. Obviously we each brought a great deal of relevant prior experience to our roles, but we still had to figure out many of the fundamentals over the first few years, often making mistakes, then recognising, fixing and finally learning from them.
Without a doubt, the most impressive learn by doing journey I’ve witnessed at Playfair — or anywhere for that matter — has been Jeevs’s. What an absolute beast she’s been over the past two years. She lost the first few deals she got through our IC when they became super competitive (that’s how VC rewards you for sourcing the best deals!) but bounced back from it with a determination to figure out what went wrong and what needs to happen differently next time so things go right. When I unexpectedly took a three month sabbatical earlier this year, she was thrust into working solo with some of my more complex portfolio companies and, showing an incredible level of resilience and on-the-fly problem solving, helped those founders overcome difficult challenges to building their businesses (and now they all want to work with her instead of me :tear).
Predicting Performance
Anyone who knows me knows I’m obsessed with figuring out how companies can hire the “best” candidates. They also know that I hate arguments from anecdote and faulty generalisations, and that I believe in the scientific method as the best strategy for attaining the most objective, most accurate, and deepest truths about facts of any kind. That’s why the recruitment process I’ve built at Playfair is grounded in science.
Part of my collection of academic papers on the science of personnel selection, which numbers around 200 unique articles.
And the science is clear that there is a single best predictor of success across every knowledge role, including the one we’re recruiting for a Playfair: general cognitive ability.
GCA is essentially the ability to learn. It is not synonymous with intelligence, which implies genetic potential. Rather, it refers to “developed ability”, or a person’s capacity for learning things through processes such as abstraction, logic, reasoning, planning, critical thinking, problem-solving and creativity. These attributes are built and strengthened by an individual from childhood onwards and we never lose the ability to continue developing them.
These skills are absolutely critical in venture capital investing, especially on a team like ours, where investors need to deploy all of the capabilities above on a daily basis to solve novel and complex problems.
Next up as a strong predictor of performance is conscientiousness, a personality trait of being disciplined, focused and responsible. Crucially, conscientiousness implies a desire to perform tasks well and to take obligations to others seriously (our steadfast duty to our founders is one of our core principles).
The lists of predictors of job performance isn’t exhaustive and I’d have to write a collection of books to list all of the ones we currently know about. For those curious to do some more research, have a look at things like: the need for achievement, possessing an internal locus of control, emotional stability, empathy and tolerance to ambiguity.
However, it is worth mentioning a few others that we’ve identified — more from personal experience than the literature — as being important to succeed as an investor at Playfair. These are a genuine interest in startups and technology, intellectual curiosity, the ability and desire to build productive networks, and humility.
As a team, we spent the first few weeks of summer doing our research, brainstorming and compiling a list of the attributes we want to assess each candidate for in our recruitment process.
The next step was to figure out the best way to conduct such an assessment.
Finding Excellence
So now that we have our 1,000+ candidates and our list of criteria against which to assess them, what next? The assessment process, of course, which looks like this.
For a rationale on this choice, we need to revisit the science (yay!). During a recruitment process, you need to figure out which characteristics predict performance, but also which methods of assessment have the highest predictive validity over those characteristics. Here’s what the science says are the best predictors:
Over the course of the assessment process, we’ll use each of these selection procedures. But before we get stuck in, a note on referrals. Simple put, we don’t accept them. Anyone who wants to be considered for a role at Playfair must apply the normal way like everyone else. As you’ll see, the entire point of the massive effort we put into our recruitment process is to level the playing field for all applicants.
Stage 1a: Resume and two basic questions
The first stage involves us manually reviewing every application (don’t feel sorry for us, we actually love this part). The goal here isn’t to identify the strongest (say top 5% of) candidates, because that would be impossible to do from looking at resumes alone, but rather to filter out those who are least likely to be amongst the strongest, as well as the more irrelevant ones (by which I mean, for example, people who live on the other side of the planet and aren’t willing to move for a one year role — this is a hybrid role with minimum of 3 days in our London office per week). By “least likely to be amongst the strongest,” I mean those who clearly don’t meet our base criteria of having a genuine interest in startups and technology or a high level of conscientiousness.
We’ve conducted over 250 interviews for investor roles at Playfair and can tell you unequivocally that if a person doesn’t already love and want to work with startups or cutting-edge technology, they’ll be out-prepared and outcompeted in the interview process by those who do. Ones lacking conscientiousness don’t put much effort or enthusiasm into their applications and probably wouldn’t, we assume, put much effort or enthusiasm into the job.
You can spot these candidates a few ways. Some have resumes that are clearly written to secure them a role in a different industry (e.g. the Summary section will explicitly say something like “Recent graduate interested in roles in fund administration”). Others won’t bother to answer the very basic questions we ask as part of the application process:
About 60% of candidates will be disqualified at this stage (but in concert with the psychometric stage below). Because it’s so easy to apply for a job these days (because we feel it isn’t fair to create a massive burden for those competing for such a limited resource), we’ll see a lot of people who don’t really know much about VC and aren’t primarily interested in finding a job in this industry.
Stage 1b: Psychometric assessment
Because we can’t humanly interview hundreds of candidates, and because we want to maintain objectivity in the process at this stage, candidates will simultaneously be sent a request to complete an online psychometric assessment, which has two parts (each 10m long):
- Critical thinking, which assesses the ability to think logically and solve abstract problems.
- Numerical reasoning, which assesses the ability to analyse numerical data and make data-driven decisions.
This general cognitive ability test uses a methodology for scoring called Item Response Theory. Candidates are compared against the average performance of all candidates that have completed the tests.
In order to present results, a T-Score is used. Unlike a percentage score that one might receive on a school exam, a T-score is based on a grading model where average performance is always “50”. Most T-scores will range between 30 and 70. A score of 30 is a very low score, a performance similar to the lowest 2% of all candidates globally. A score of 70 marks a performance better or equal to 98% of all candidates.
When all of the results are in, we’ll see them plotted on a chart showing a normal distribution. Here’s an example of what a score for a candidate will look like (not a real candidate):
We’ve done some testing over the last few recruitment processes we’ve run at Playfair, where we randomly picked candidates from the left three quartiles to progress to the next stage of the interview process. These cohorts always perform significantly worse than those in the right quartile and no individual from within them ever got to the final interview stage.
At this stage of the process, most companies would progress the top ten performing candidates to the next stage of the process. But we want to give more people the opportunity to show us what they’ve got and to interact (if even very briefly) with a member of the Playfair team. As such, we tend to progress candidates (alongside the questions and resume) who fall within the top quartile of psychometric results to the interview stage. It’s worth noting that we expect there to be a small percentage (~10%) of candidates who don’t decide to spend the time (20m) to complete this part of the process and we’re comfortable this is a natural filter for those who are passionate and certain enough about a career in venture and at Playfair to spend this time.
The sheer volume of interviewing 50 candidates, including prep and wrap-up, that Chris will do at the next stage means he’ll have to substantially clear his schedule for three weeks in February just to get through all of the candidates.
Finally, it’s worth noting that hereafter we don’t review any candidates’ psychometric scores, as we feel everyone who progresses beyond this stage has sufficient brain power to perform to the standard required by the role. At this stage, the difference maker will be other important characteristics.
Stage 2: 30-min video interview with Chris
Here’s where candidates get to meet a member of the team and ask questions about what life at Playfair is like.
We use structured interviews at this stage. When I blogged about structured interviews back in 2016, the science was clear on their superior predictive validity versus unstructured interviews. However, the application of a new, more accurate testing method has changed this conclusion. As you can see from the table above, structured and unstructured interviews have an identical operational validity, with structured interviews being slightly better predictors when paired with tests of GCA.
Notwithstanding, I still prefer structured interviewing at this stage because in my experience they are better at controlling for interviewer biases.
Chris will interview 30–50 candidates at this stage, asking candidates a number of randomly selected questions from a question bank we’ve prepared. The questions will be based on the specifics of the role itself and will generally take the following form: “can you give me an example of a time when you’ve done X?”
Chris is required to take detailed notes of each candidate’s answers, as well as to provide an analysis of the candidate’s performance and a score based on a similar rating scale we use to review pitches.
The reason for detailed note-taking is that, once the interviews are concluded Chris and Henrik will sit down together and review each application, looking in detail at each candidate’s resume, their answers to the written questions and the detailed written feedback. This provides an opportunity to spot any biassed analysis and scoring by the interviewers, given that the other reviewer will not actually have met the candidate yet.
Stage 3: 1-hour video interview with Henrik: “The Deck Review”
Because we value the importance of grounding our interviews in what the daily life of an analyst on our team will be like, the next stage will involve candidates collaboratively reviewing a bunch of real-life pitch decks with Henrik with a view to answering one specific question: should we take a call with this founding team? At Playfair, we receive hundreds of pitch decks every month but, as is the case in our recruitment process, we only have the capacity to speak with a few of the founding teams. One of the main responsibilities of an analyst at Playfair is to work closely with Sheff, Clare and João to screen all of our inbound deals and decide, based both on objective criteria and their unique subjective assessment, which ones we should take a call with.
Stage 4: Final, onsite interview: “The Memo Task” + coffee with the team
The last stage of the process involves a take home task. Here we’ll present the candidates with a pitch deck (that a founder friend of ours has kindly agreed to let us use), a few days before coming back to our office, and ask them to write one or two sections of a short-form investment memo. Gathering information from disparate sources and summarising and analysing it in an elegant manner is another important duty of an investor at Playfair.
This stage is also about seeing how well a candidate works independently and without much guidance. When the candidates visit us again, they’ll take part in a mock investment committee meeting where we’ll discuss the work they’ve done and ask them to elaborate on certain aspects of it.
And finally, each candidate will have a coffee interview with other members of the team they haven’t yet had a chance to meet.
After this, we’ll convene a final hiring committee meeting and decide which candidate we’ll extend an employment offer to.
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And that’s it! It’s not a perfect recruitment process, as none ever is; but so far it’s worked for us.
Finally, for those candidates reading this and thinking what a slog this will be, please keep in mind that we only ask you to do one stage at a time and that each interview will be a great opportunity to practice your interviewing and learn a bit more about Playfair and our team.
