What would a "super résumé" look like?
A brief update on what I've been building, and thoughts on why hiring well remains such a hard problem.
Hi friends,
As I mentioned last time, I'm back - with screenshots.
As a reminder, my current mission is to help leaders make informed decisions regarding hiring and building their teams. Our flagship product, Tenarch, has been dedicated to providing leaders with better hiring data, and we have made significant progress in the past year.
One of the key challenges for hiring managers is the inherent limitations of resumes as a source of information. They’re the backbone of the whole hiring process - influencing who you talk to and what you talk with them about. However, resumes lack the depth and context to truly understand why a candidate is a good fit for your team. Additionally, they mostly focus on historical achievements, neglecting to address a candidate's future goals and how they align with yours.
Because they don’t give you enough signal, you have to puzzle it out for yourself, and the heuristics we use to fill in the gaps and the assumptions we make about the relevance of what is there are major sources of unconscious bias.
You have almost certainly passed on a fantastic hire because you didn’t get the signals you needed from their resume. However, the solution isn’t “better resumes” - it’s more contextual data.
Not all data is created equal, of course. There are key factors for “what would we want to know about a candidate” - to both decide to interview them and also decide what to focus on in the interview. Our research and evidence focused us on three crucial factors: Team Fit, Role Fit, and Work History.
Team Fit
The level of alignment between your team's working styles and the candidate's preferences. Sometimes called "culture fit" or "values fit” - however this kind of language often creates more ambiguity than it solves. To be clear, hiring for team fit is critical - research shows that people who work at organizations where they are a mutually good fit perform better and stay longer. It just needs to be evidence-based and not based on feel-good slogans or ambiguous ideas like “work hard, play hard.” We have invested in productizing the research around team fit and can reliably help teams and candidates quantify it in a way that makes sense to them and leads to deeper insights in the hiring process.
Role Fit
The level of alignment between what you need from the role and the candidate's experience and preferences. Things like the candidate’s level of seniority, skills, and work context. Often there is ambiguity here - both on candidate profiles and what the hiring manager and team are looking for. By structuring the needs of the team and the candidate’s preferences and qualifications, we’re able to provide a detailed opinion on where a particular candidate lives within several dimensions of role fit:
seniority and context - often one of the hardest areas to assess - what does a particular seniority really mean, in terms of ability, scope, and impact, and how much does does that change across different business contexts?
aspects and tech stack - for engineering roles, how much alignment is there between a candidate and the desired technical skills and knowledge, and how much relevant context do they have in your technical domain?
preferences - crucially important and missing from almost all resumes - even if they meet your criteria on paper, is this the work they want to do going forward?
These are all things that hiring managers would have to parse from role titles and descriptions, with extremely mixed results due to lack of context, data, and unconscious bias. With more structured data, we find that hiring managers can ask better questions and make better decisions on screening and interviewing. Our acceptance rate for introduced candidates to interview is > 90%.
Work History
What most people think of when they think of a resume. Past companies and roles, with some high-level context. It wouldn’t be a resume without it - but it’s woefully insufficient without the data on role fit and team fit. Here we’re having the candidate tell their story and providing important context clues for hiring managers based on our research into the key factors they’re most curious about for each role - not only what they did but what the context of their work was - things like customer focus, scale, and team size.
Making it all work
We’ve pulled all this data into the Tenarch talent profile - the kind of in-depth report on each candidate I wished I had when building teams but always found too expensive or time-consuming.
We’ve used these profiles to help leaders at startups and scaleups find and hire people that they otherwise would have missed out on for lack of data. I’ve also been really happy that the feedback on the way we gather this data from candidates has been both delightful and insightful for them - the typical candidate experience is so awful; it’s great to be able to provide something that is both positive and unique.
On the company front: we've had a rough first half of 2023 - as expected. We've been focused on helping founders and engineering leaders scale up their teams - from pre-seed through Series C/D generally. We've seen companies trying to get by with less - put off hiring to slow burn rates or reach certain milestones.
At the same time, we haven't seen any abatement in demand for quality senior+ level engineering talent. Most folks are still receiving a lot of interest and multiple offers - even if it now takes slightly longer to go through the search process. This isn't a situation where there is a glut of talent in the market, so I've been recommending that leaders continue to be strategic with their hiring right now - as it's an opportunity to pick up talent with (slightly) less competition.
What’s next
I'm in the market for new companies to work with, so if you know anyone who wants to grow their team(s) and is struggling with the general brokenness of traditional hiring methods, send them my way.
I'm also looking to grow my engineering team by 1 - so if we've worked together in the past and you're looking for something new - send me a message. I'd love to talk with you.