I recently sat down with Nadia Singer, Chief People Officer at Figma, to unpack what separates good interviewers from truly remarkable talent evaluators. As I reflect on my own hiring philosophy in product management leadership, her approach sharpened my lens for identifying signal over noise, eliminating bias, and scaling culture with intention.
Nadia joined Figma in 2020 and has seen explosive growth in her own career alongside the collaborative design platform’s. Before Figma, Singer was a talent expert who has hired hundreds of talented folks at places like Quora, Facebook and Google.
In our discussion, we dove into the patterns that consistently predict excellence. What resonated most was a simple yet powerful idea from her recruiter playbook: study how a candidate reaches an answer, rather than what they say. I’ve found this especially impactful when hiring PMs and cross-functional leaders. Rather than celebrating the “right” conclusion, I push candidates to narrate their reasoning, make trade-offs explicit, and surface assumptions—revealing structured thinking, customer empathy, and learning velocity.
To operationalize this, I ask candidates to walk me through ambiguous product decisions: Which constraints did you prioritize and why? Where did you seek disconfirming evidence? How did you iterate when new data emerged? I’m listening for clarity of problem framing, the ability to quantify impact, and the rigor of decision-making under uncertainty. The outcome matters, but the method matters more.
We also explored tactics interviewers can use to avoid pattern matching and other biases. In my teams, that starts with a role scorecard that defines the core competencies up front (not resume proxies), structured interviews with consistent prompts, and independent scoring before any debrief. I’m deliberate about diverse panels, rotating interviewers to reduce shared blind spots, and separating signal (evidence-backed behaviors) from story (polish, pedigree, or charisma). In debriefs, the most senior voice speaks last, we anchor on evidence tied to the scorecard, and we explicitly call out potential biases when they appear.
Another theme was learning from early missteps in recruiting. I’ve made many of the common mistakes: over-indexing on pedigree instead of proof of outcomes, letting hypotheticals outweigh real-world execution, asking leading questions that telegraph the “desired” answer, and failing to define success criteria before meeting candidates. The fix is discipline: better prompts, deeper follow-ups (“tell me about a time…” with measurable results), consistent rubrics, and a higher bar for reference checks that validate how someone collaborates under pressure.
Finally, we discussed ways that Figma tweaked its approach to culture so it could scale alongside the company. My takeaway: culture scales when it’s operationalized. Codify a few non-negotiable principles, translate them into observable behaviors, and weave them into hiring rubrics, onboarding, performance management, and rituals like product reviews. As the organization grows, refine language—without diluting standards—so new teams can apply the same principles to different contexts.
If you lead hiring for product or adjacent functions, here’s the throughline I’m taking forward: raise the bar on reasoning, not rhetoric; design interviews that produce comparable evidence; and treat culture as a living operating system, not a poster. That’s how you consistently spot high-agency, high-learning talent—and build teams that compound value over time.
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