The five traits that define the right talent, and how to hire for them

What you’ll learn

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• Why credentials and experience don’t predict performance the way we think they do

• How to evaluate ownership mindset before someone joins your team

• What collaboration means in distributed work (it’s not about being nice)

• Why judgment matters more than technical skills as AI reshapes work

• How learning agility predicts long-term success better than existing knowledge

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You’ll often hear talent platforms claim they deliver the “top 1%” or “top 3%” of the world’s talent. We don’t use that language. It’s a marketing narrative. Admittedly, it sounds impressive but offers little practical value when you’re actually building teams.

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If only the top 1% of people were capable of doing great work, most successful companies simply wouldn’t exist. What matters is hiring the right talent, in the right way, for the problems you’re trying to solve for today and tomorrow.

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Here’s what we’ve learned after twenty years of hiring across roles, teams, and stages of growth: the right talent isn’t defined by rankings, credentials, or years of experience. It’s defined by five core traits that consistently predict performance. When these traits are present, teams move faster; decision-making improves, friction drops, and performance compounds over time.

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This is the foundation of our PolyMatch approach. A practical system for identifying talent based on what actually matters.

Why credentials don’t predict performance

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An MBA doesn’t guarantee someone can build an executable roadmap. Ten years of experience might be the same year repeated ten times. A degree from a top school might mean someone interviewed well at age twenty-two, not that they can deliver the specific outcomes your role requires today.

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Credentials are proxies, not predictors. They’re signals we’ve relied on because they’re easy to list in job descriptions and easy to scan on résumés, but they don’t reliably tell you whether someone can do the work.

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Here’s the disconnect. When you list “Senior IT Leader with an MBA” as a requirement, what you really need is someone who can set a clear technology roadmap, prioritize initiatives against business goals, and guide the organization through trade-offs as constraints change. The MBA is shorthand for strategic thinking and business acumen, but it doesn’t guarantee either. Plenty of people have MBAs and can’t translate strategy into an executable plan. Plenty of people without MBAs can.

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This becomes especially obvious when hiring distributed teams. You can’t rely on proximity to cover up gaps. A brilliant engineer who can’t communicate proactively creates drag in a remote environment. A solid engineer who communicates context, surfaces blockers early, and thinks about how their work affects others moves everything forward. The credential doesn’t tell you which one you’re getting.

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What predicts performance is evidence of delivering outcomes, not possessing titles, degrees, or tenure. When you focus on credentials instead of outcomes, you end up looking for purple squirrels that don’t exist, and you miss exceptional people who don’t fit the mold.

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The five traits that truly matter

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There are many frameworks for evaluating talent. What makes these five traits powerful is that they’re predictive across roles and contexts, observable and testable through structured interviews and real-work assessments, independent of credentials or pedigree, and foundational without being comprehensive. If someone has these five traits, they’ll likely succeed. If they’re missing two or more, they’ll struggle no matter how impressive their résumé looks.

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1. They can do the work (outcomes, not proxies)

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This is the baseline. Everyone wants to hire people who can do the work well. Where things break down is what gets evaluated when hiring.

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Too often, hiring starts with a list of qualifications rather than a clear understanding of what the role requires. Roles are frequently defined by proxies. Proxies refer to things like years of experience, degrees from top schools, and long lists of “must-have” skills. These are easy to list, but they don’t reliably predict performance.

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What you want to evaluate is whether someone can deliver the outcomes the role exists to achieve.

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We start by asking: what does success look like in this role six months from now? Then we work backward to identify the capabilities required to get there. This is exactly what a Position Blueprint helps you define.

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Instead of listing “must-have” skills, we clarify outcomes. Will this person need to build something from scratch, scale something that already works, or fix something that’s broken? Will they need to influence stakeholders, manage a team, or execute independently?

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Once these outcomes are clear, we evaluate the candidate. Has this person done work that produced similar outcomes before? Can they explain their process, the trade-offs they navigated, and what they learned? Do they understand why certain decisions led to certain results?

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We also test directly. For technical roles, we assign real problems similar to what they’d face on the job. For non-technical roles, we use case exercises or scenario-based discussions that reveal how they think, prioritize, and make decisions.

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This approach surfaces whether someone can do the work, not whether they can talk about doing it.

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2. They show ownership thinking

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Ownership is the difference between someone who completes tasks and someone who takes responsibility for outcomes.

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People with strong ownership see their work through end-to-end. They don’t just execute what’s asked. They think about whether it’s the right thing to do, whether anything is missing, and what happens after the work is done. They surface problems early instead of waiting to be asked. They follow through without constant oversight.

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This matters more now because modern work is less structured than it used to be. Roles are fluid, priorities shift, and the best path forward isn’t always obvious. People who wait to be told what to do slow teams down. People who take ownership move work forward.

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Ownership is demonstrated by the way people approach problems. When something isn’t working, do they raise it or hope someone else notices? When requirements are unclear, do they ask clarifying questions or make assumptions and build the wrong thing? When a project hits a roadblock, do they escalate early or wait until it’s too late to course-correct?

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How people communicate can also demonstrate ownership. Do they provide context and reasoning, or just status updates? Do they connect the dots on how their work affects others, or do they treat everything as isolated tasks?

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This isn’t about working longer hours or doing other people’s jobs. It’s about taking responsibility for outcomes, not just activity. And it’s completely independent of seniority. Some junior people naturally think this way, while some senior people don’t.

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We ask candidates to describe times when they took initiative on something that wasn’t clearly their responsibility, times when something went wrong and what role they played, and times when they had to make progress without clear direction.

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Do they take responsibility or shift blame? Do they see beyond their immediate tasks to the bigger picture? We listen to identify whether they follow through or hand things off and hope for the best.

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People with strong ownership don’t need to be prompted. This trait will show up consistently in how they talk about their work.

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3. They work well with others

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Individual brilliance doesn’t create team success. Collaboration does.

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This isn’t about being nice or agreeable. It’s about whether someone can work effectively with others to achieve shared goals. Can they communicate clearly? Do they push back constructively when they disagree? Do they help others succeed, or operate in isolation?

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In distributed teams, collaboration becomes even more critical. Without proximity, misunderstandings escalate faster, silos form more easily, and small friction points compound into real dysfunction. The people who succeed remotely aren’t just technically capable; they’re capable of working well across distance.

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Collaboration means clear, proactive communication. It means providing context, not just updates. It means asking clarifying questions when something is unclear instead of guessing. It means surfacing problems early instead of hiding them until they’re catastrophic.

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Collaboration also means productive tension. The best teams are good at resolving disagreements. People who collaborate well can disagree respectfully, advocate for their position, and ultimately align on a decision even if it wasn’t their first choice.

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It doesn’t work out when people avoid conflict entirely or can’t take feedback. Those who need to be right instead of focusing on outcomes are people who don’t think about how their work affects others.

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We ask about times when collaboration didn’t go smoothly, times when they disagreed with teammates or stakeholders, and times when they had to explain something complex to someone without a technical background.

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We’re listening to detect self-awareness. Do they acknowledge their role in challenging relationships, or is it always the other person’s fault? Do they adapt their communication style to different audiences, or expect everyone to meet them where they are?

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For remote roles, we also evaluate communication quality directly. Can they explain their thinking clearly in writing? Do they provide enough context for async collaboration? Do they structure their thoughts in a way that’s easy to follow?

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4. They demonstrate strong judgment and problem-solving

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As work becomes more complex and ambiguous, judgment matters more than technical execution.

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Judgment is the ability to make sound decisions when the right answer isn’t obvious. It’s knowing how to assess trade-offs, evaluate incomplete information, and choose a path forward despite uncertainty. It’s understanding when to move fast and when to slow down, when to ask for help and when to decide independently, when to push back and when to align.

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This matters because the problems worth solving are rarely straightforward. Requirements change. Constraints shift. What seemed like the right approach yesterday may not be the right approach today. The people who navigate this well are the ones with the best judgment.

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Judgment shows up in how people frame problems. Do they understand the real problem they’re solving, or are they solving the surface issue? Do they consider second-order effects, or just the immediate outcome?

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Judgment can be evaluated by evaluating how people handle ambiguity. When requirements aren’t clear, do they clarify upfront or build first and ask questions later? When priorities conflict, do they make thoughtful trade-offs or just do what’s easiest?

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Judgment is also demonstrated by how people learn from outcomes. When something doesn’t go as planned, do they reflect on why and adjust their approach? Or do they repeat the same mistakes?

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We ask about times when they had to make decisions without having all the information they wanted, times when they chose between competing priorities, and times when they had to solve problems that didn’t have obvious solutions.

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We listen to their process. How did they think through the decision? What factors did they consider? What would they do differently now? The quality of their reasoning matters more than whether the decision worked out perfectly.

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5. They show learning agility

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The ability to learn matters more than what someone already knows.

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Learning agility is about how quickly someone picks up new concepts, adapts to change, and applies new thinking to real work. It’s about whether someone gets better over time or stays static. And it’s about whether someone seeks feedback and growth or avoids discomfort.

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This matters because the shelf life of specific skills is shrinking. The tools, frameworks, and technologies that define a role today may not be relevant in two years. The people who succeed long-term aren’t the ones who know the most right now, they’re the ones who can learn what they need, when they need it.

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Learning agility can be observed by how people respond to feedback. Do they get defensive, or do they reflect and adjust? Do they seek feedback proactively, or avoid it?

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How do people handle new challenges? When they encounter something unfamiliar, do they figure it out or wait to be taught? Do they ask good questions, or expect others to explain everything?

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It also shows up in self-awareness. Do people know their strengths and weaknesses? Can they articulate what they’re working on improving? Do they take responsibility for their own growth?

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We ask about times when they’ve received difficult feedback, times when they had to learn something new quickly, and times when their role or responsibilities changed significantly.

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We listen to detect openness. Are they willing to share feedback that was hard to hear? Do they talk about what they learned, or just defend their actions? Do they demonstrate curiosity and growth mindset, or do they present themselves as already having all the answers?

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How AI changes what matters in hiring

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AI is changing what matters in hiring, but not in the way most people think.

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As AI handles more routine tasks and technical execution, the value shifts to the thinking that surrounds the work problem. Things like framing, systems-thinking, communication, and judgment. The people who can frame problems well, exercise sound judgment, and think in systems will determine what gets built and whether it works.

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This makes the five traits even more important. Technical skills are easier to teach and automate. Ownership, collaboration, judgment, and learning agility are harder to develop, and they’re what will separate high performers from everyone else.

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The gap between a great hire and an average one is widening. AI is increasing individual productivity, which means the person who can frame problems well, think in systems, and exercise sound judgment will determine what gets built. Technical execution is getting easier to automate. Clear thinking and communication are not.

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When we evaluate candidates, we’re not just asking “Can they do this job today?” We’re asking, “Can they adapt as the job changes, as tools evolve, and as AI reshapes how work gets done?”

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The people who thrive in that environment are the ones who demonstrate these five traits consistently.

What changes when you hire this way

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When you evaluate candidates for these five traits consistently, a few things happen.

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Hiring decisions become clearer. You’re comparing candidates on the same criteria, not on gut feel or who made the best impression.

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Quality improves over time. You’re selecting based on traits that predict performance, not proxies that sound good but don’t actually matter.

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Bias diminishes. When you focus on evidence of outcomes, ownership, collaboration, judgment, and learning, you’re less likely to be swayed by things that don’t predict performance, like where someone went to school or how polished they are in interviews.

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Onboarding goes faster. People with these traits ramp up faster because they take ownership, ask good questions, and learn quickly.

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Retention improves. People who demonstrate these traits tend to be engaged, growth-oriented, and aligned with high-performing teams. They stay longer because they’re successful and valued.

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One strong hire who demonstrates all five traits doesn’t just contribute individually. They raise the bar for everyone around them. One weak hire missing multiple traits creates ongoing drag that’s hard to undo. The five traits compound over time.

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Putting this into practice

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This framework isn’t theoretical. It’s what we use every day to evaluate candidates in our PolyMatch process.

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We start by understanding the outcomes the role requires. Then we design an evaluation process that surfaces evidence of each trait through structured interviews, real-work assessments, and behavioral questions that reveal how someone actually operates.

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We don’t rely on credentials, years of experience, or gut feel. We are looking for evidence. And over time, that evidence compounds into better hiring decisions, stronger teams, and measurably better outcomes.

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This framework reflects what we’ve seen to work consistently across teams, roles, and stages of growth. When these traits are present, teams move faster; decision-making improves, friction drops, and performance improves over time.

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Communication skills predict remote success more than technical skills. In distributed teams, communication is the primary foundation. A brilliant engineer who can’t communicate proactively creates drag. A solid engineer who communicates well moves work forward.

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Learning agility matters more than what someone knows today. Hire for how quickly someone picks up new concepts, not what they already know. This is why the 30% stretch rule works. Hire for the traits and the capacity to grow into the role.

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If you’re building or scaling a tech team and want help finding people who actually demonstrate these traits, reach out. We’ve spent years refining how to evaluate candidates for outcomes, ownership, collaboration, judgment, and learning agility, especially for distributed teams where these traits matter most.