A successful startup is fundamentally a group of people who execute well together. Building a viable product is key, but changing the product is easier than changing the founders. If you’re considering a role at an early-stage startup, you will be investing your best waking hours in the company. It’s important to assess the team before making such an investment.
To learn about the team, ask questions from the People section of my Data-to-AI Health Check for Startups. Some questions emphasise probing Data/AI/ML capabilities – an area that’s often misunderstood by non-specialists. However, this emphasis can be shifted to different functional areas as needed. Similarly to my previous post on scrutinising the Product & Business Model, the rest of this post lists my questions along with brief opinionated explanations.
People questions
Q1: Who are the founders? What are their skills and experience? Founders make or break a startup. It’s important to gain confidence that they have the skills and experience required to build the company, along with the mindset needed to keep learning and developing relevant skills. Founders who were previously successful are an especially positive sign – it indicates that they have the persistence and flexibility needed to build a business.
Q2: What motivates the founders? How passionate are they about the startup’s problem space? My favourite founders are those who build a business based on their deep understanding of customer problems in an area they deeply care about. For example, I previously worked with Orkestra – a software-as-a-service startup that grew directly out of the founders’ experience as consultants. In Orkestra’s case, the founders had already spent years working together solving customer problems prior to founding the company. By contrast, some startups are founded by near-strangers just because the founders want to build something – a red flag.
Q3: Have any founders left? Why and how? Startups can turn friends into foes. Foes holding a significant share of the company may lead to its destruction. But even in cases where founders leave on good terms without significant equity, their departure stories help understand founder personalities and the trajectory of the company. For example, if you’re considering a full-time position, and the story of how the remaining founders treated departing founders gives you pause, you’re better off working elsewhere.
Q4: Who are the key employees? Early employees are almost as important to startup success as founders. In fact, I know of multiple cases where employees “became” founders even though they weren’t there from day one. For example, after my PhD I joined Giveable as a founding data scientist. As the first employee, I was in charge of building the backend for Giveable’s B2C gift recommendation web app. Due to market conditions, we pivoted to a B2B recommender-as-a-service offering – not what the original founder had envisioned. He decided to move on, and I was left with much more equity than originally planned, along with the rights to the code. While I could have kept going as a “founder”, I decided to use the codebase to continue building the same B2B product as part of a more established ecommerce startup.
Q5: Have any key employees left (including involuntarily)? Why and how? With early employees being almost as important to startup success as founders, stories of their departures can be as informative as stories of founder departures. If you’re considering a startup job, these stories can tell you a lot about founder-employee dynamics, before you become an employee. If you’re especially thorough, you can even reach out to the former employees to get their side of the story. Positive signs include low employee turnover and founders who are comfortable with you speaking to their former employees.
Q6: How committed are the founders and key employees (partly measured by work time spent on the startup)? Early on, it’s common for founders and employees to be involved on a part-time basis. This is fine, but if you’re going to commit a significant chunk of your time to the startup, you need to know who you’ll be working with. As an employee, you can usually ignore the big names that are listed as advisors on the startup’s website – their involvement is typically minimal. That said, both advisors and fractional contractors provide access to expertise and connections that may not be necessary on a full-time basis. In fact, fractional help is much better than premature hiring, which unnecessarily burns through funding. The main things to look at in an answer to the commitment question are: (1) transparency; and (2) that committed staff have the skills needed to achieve the next milestones.
Q7: What hiring practices do you follow? How do you assess the skills of new experts (e.g., first data hire)? Given the importance of early employees, a loose hiring process is a cause for alarm. However, thoughtlessly borrowing hiring practices from the likes of Google is also problematic, as such processes are laughably hackable and tedious to everyone involved. Startups can and should move faster on hiring than established players: My favourite hiring processes include paid work on real problems after an initial low-cost filter. These are hard to scale, but there’s no need to scale hiring in the early days. Paying for work on real problems also helps address the challenge of assessing the skills of new experts – they are judged on real work output rather than on confidence, pedigree, and performance on convoluted tasks.
Q8: Do you pay market rates? Startups that don’t pay market rates are best avoided. They’re unlikely to attract and retain quality employees. Founders of such startups may also fall victim to classic fallacies like the 1975 Mythical Man-Month, and make expensive mistakes like hiring two mediocre engineers in place of one excellent engineer. When it comes to software (and data) development, higher quality often incurs a lower overall cost. Paying market rates and hiring great people is the way to go, especially in the age of AI-powered interns.
Q9: Are there any critical skill gaps among current personnel (especially around data/AI/ML)? If you’re asking this question as a candidate, you’re probably going to fill one of the gaps. However, gaps are relative to what the startup is trying to do. For example, if they have ambitious AI/ML plans that require a range of data skills they don’t have on the current team (from data engineering through data science to AI/ML engineering), they better be planning to hire more than one junior data generalist.
Q10: What’s the hiring roadmap for the next 6-12-24 months? How will it affect the runway? Is it dependent on new funding or revenue growth? Startup founders usually have grand plans – that’s what you want from founders! But plans for 12-24 months are often in the realm of wishful thinking, and a lot can change even in six months. As a candidate, try to get a realistic view of the hiring that is highly likely to happen, along with the hiring that is dependent on new money coming in. Assuming that the latter doesn’t happen due to a cashflow crunch, would you still take the job?
Even more questions?
This post is part of a series on my Data-to-AI Health Check for Startups. Previous posts:
- Assessing a startup’s data-to-AI health: Overview and motivation
- Business questions to ask before taking a startup data role
You can download a guide containing all the questions as a PDF. The next area I’ll cover is Culture – how people work together. Feedback is always welcome!
Public comments are closed, but I love hearing from readers. Feel free to contact me with your thoughts.