How to Hire for a Role That Won't Exist in Six Months

At fifteen people, every hire is obvious. You need someone to run marketing. You hire a marketer. You need someone to handle customer support. You hire a support person. The job description writes itself because the gap is visible.

At forty people, the gaps multiply faster than you can name them. And the role you hire for today will not be the role that person does six months from now. If you are hiring against a static job description at Series A, you are optimizing for the wrong version of the role.

The head of customer success you hire today might be running partnerships by next year. The operations hire who joins to manage your office might end up owning your entire onboarding function. The marketing generalist might specialize into demand generation or product marketing depending on where the company bets next. This is not a failure of planning. It is the natural state of a company whose strategy is still forming.

The problem is that most Series A hiring processes are designed for stability. You write a job description. You source against it. You screen candidates for the skills listed. You make an offer. And three months later, the job has changed so much that your carefully selected candidate is underqualified for the new reality or, worse, unwilling to adapt to it.

Here is a better approach: a Role Evolution Scorecard that evaluates candidates across three dimensions. Current competency tells you whether they can do the job today. Adjacent capability tells you whether they can grow into the next version. And learning velocity tells you how fast they will close the gap.

Dimension 1: Current Competency (Weight: 30%)

This is the dimension most hiring processes already cover. Can the candidate do the core work listed in the job description? For a head of customer success, that might mean designing onboarding flows, standing up a help desk tool, or building a customer health scoring model.

Score this dimension honestly. A candidate does not need to check every box, and at Series A they rarely will. But you need a baseline: can this person deliver something useful in their first thirty days?

The key difference from a standard interview: do not let this dimension dominate the scorecard. At a 500-person company, current competency might be 70% of the hiring decision. At Series A, it should be 30%. The role is going to change too fast for today's skills to carry the majority of the weight.

Interview question: "Tell me about the last time you were hired into a role where the day-to-day work ended up being meaningfully different from what was described in the interview process. What surprised you, and how did you adapt?"

Dimension 2: Adjacent Capability (Weight: 40%)

Adjacent capability is the hardest dimension to evaluate and the most predictive of Series A success. It measures whether a candidate has done work that sits next to the role you are hiring for, even if the job title was different.

A customer success leader who has run account management has adjacent capability. They already understand the commercial side of the relationship. If the CS function eventually rolls up under revenue, they will not be starting from zero.

An operations hire who has built internal tooling in Airtable or Zapier has adjacent capability for product operations. If your ops function starts bleeding into product, they have a ramp.

A marketing generalist who has written content and run paid campaigns has adjacent capability for either specialization track. When the company decides which channel to double down on, they can go either direction.

To evaluate adjacent capability, map the most likely evolution paths for the role before you write the job description. If you are hiring a head of support, what are the three most likely things this role could expand into? Cross-sell? Product feedback loops? Community management? Score the candidate against those expansion paths, not just the immediate need.

Interview question: "Here are three ways this role might evolve over the next eighteen months. For each one, walk me through the part you would feel most equipped to handle and the part where you would need the most support."

Dimension 3: Learning Velocity (Weight: 30%)

At Series A, the domain expertise you need in twelve months may not exist yet in any candidate. So you hire for learning velocity: the speed and depth with which someone acquires new mental models.

Learning velocity is not about whether someone took a course or got a certification. It shows up in how they process unfamiliar information during the interview process itself.

Signal 1: They ask better questions as the conversation progresses. A high-learning-velocity candidate starts a 45-minute interview asking surface-level questions about your product. By minute 30, they are asking questions that show they have internalized the surface-level answers and are now probing the underlying assumptions. Watch for the quality curve of their questions.

Signal 2: They can map a concept from their domain to yours. Ask them to explain a framework from their previous industry and then apply it to your business. A candidate with high learning velocity will not just translate the terminology. They will identify where the analogy breaks and adjust their thinking in real time.

Signal 3: They are specific about what they do not know. Low-learning-velocity candidates bluff. High-learning-velocity candidates say, "I do not know enough about that to have an opinion yet, but here is how I would go about learning it." The second answer is worth ten times more than the bluff.

Interview question: "Pick a topic you knew nothing about two years ago and now consider yourself fluent in. Walk me through the process. How did you learn it? What did you get wrong early on? When did it click?"

Putting the Scorecard Together

Score each dimension on a 1-to-5 scale. Multiply by the weights. Set a minimum threshold for each dimension individually, not just a composite score. A candidate with a 5 in current competency and a 2 in learning velocity is a bad Series A hire, even if the math averages out.

Dimension	Weight	Score (1-5)	Weighted
Current Competency	30%	___	___
Adjacent Capability	40%	___	___
Learning Velocity	30%	___	___
Composite			___ / 5

Minimum thresholds: 3 on every dimension. Composite of 3.5 or higher to advance.

This scorecard will surface candidates who look risky on a standard job description but are actually the safest bets for a company whose roles will not sit still. The marketing manager with zero demand gen experience but a track record of picking up new channels in under ninety days. The operations lead who has never run onboarding but has built and rebuilt internal processes across three different functions. These are the people who thrive at Series A, and a static job description will filter every single one of them out.

The scorecard also forces honesty in the hiring process. When a hiring manager wants to make an offer to someone with a 2 in learning velocity because they "really know the space," the scorecard makes the tradeoff explicit. You can still make the hire. But you are now making it with your eyes open.