Pearl Talent - Assessment Psychometrist

Pearl Talent - Psychometrician We re hiring a Psychometrician for Pearl which finds exceptional talent from around the world trains them to be AI-native and places them into operational roles at startups as managed contractors from client-facing roles to software engineers to executive assistants. We re 3x founders who ve bootstrapped our company to a couple million in ARR and are adding six to seven figures in net new annualized revenue each month. Our clients span venture-backed tech and healthcare including fast-growing startups and phenomenal US-based businesses that have raised over $3B in funding from Sequoia a16z Founders Fund Y Combinator and other top VC firms. Today we re roughly 50 people managing a few hundred talents growing fast and into new verticals. We started Pearl because we believe that even though opportunity isn t created equal in the world ambitious talent is.

Location

Fully remote Purpose of Your Role You ll be the scientific backbone of our AI voice-model initiative. We re building a model that infers psychological constructs from voice and its quality will only ever be as good as the measurement system behind it. This is not a machine-learning engineering role. You won t build the model itself. Instead you ll define what we should measure design and validate the instruments that measure it and convert those measurements into defensible ground-truth labels and evaluation criteria for model training. You ll also be the person in the room with the authority — and the responsibility — to say what voice can t responsibly tell us about a person. What You ll Own

• The Psychometric Framework Review the literature and evaluate established frameworks (Big Five/OCEAN HEXACO DISC and other relevant models) for validity usefulness and fit for our use case Recommend which constructs and dimensions to include exclude or treat cautiously — and explicitly identify traits that cannot be responsibly inferred from voice Build and maintain a clear construct map connecting constructs dimensions indicators survey items and resulting scores documented as the single source of truth for Research Data and AI teams
• Instrument Design and Validation Design psychometric surveys that serve as a high-quality measurement source for participants who also provide voice samples items response scales scoring rules reverse-coded items and attention checks while minimizing fatigue and response bias Decide when to use validated existing scales adapt them or develop fit-for-purpose measures — then pilot and iterate based on empirical performance Run the full validation battery reliability (McDonald s omega Cronbach s alpha item-total analysis) EFA and CFA to test dimensional structure construct/convergent/discriminant/criterion validity test-retest where appropriate and IRT where useful Recommend sample sizes pilot methodology and evidence thresholds a measure must clear before it s used for training
• Ground Truth and Model Evaluation Partner with AI and Data teams to transform psychometric responses into defensible training labels continuous scores categories normalization and confidence/reliability information Define rules for missing inconsistent or low-quality responses and the criteria for when a label is reliable enough to train on Design the framework for comparing survey-based ground truth against voice-model predictions and define evaluation metrics that reflect psychometric validity — not just ML performance Analyze where the model performs well where it fails and whether prediction quality differs by construct or population
• Bias Fairness and Scientific Guardrails Evaluate measurement invariance and potential bias across languages cultures and populations including the impact of translation and sampling choices Define the scientific limits around what conclusions may and may not be drawn from voice-based predictions and partner with Product and AI to prevent unsupported or misleading interpretations Translate complex statistical findings into practical decisions for technical and non-technical stakeholders keeping methodology aligned with current peer-reviewed research What Success Looks Like Within your first cycle the full measurement pipeline runs through you framework selected construct map built Survey V1 designed and piloted statistically validated refined into Survey V2 and converted into a scoring system that produces training labels the AI team trusts. When the voice model ships predictions they re validated against ground truth you built — and every claim the product makes about a person is one your framework can scientifically defend.

Requirements

Advanced degree (MSc or PhD preferred) in Psychometrics Quantitative Psychology Psychological Measurement I/O Psychology Behavioral Science or a closely related quantitative field Hands-on instrument experience. You ve designed validated and refined psychometric instruments yourself — not just used them A strong statistical foundation in factor analysis reliability validity and measurement theory IRT experience is a strong plus Comfort with messy real-world data. You can work with imperfect behavioral datasets and still make evidence-based recommendations Cross-functional fluency. You re comfortable collaborating with AI/ML engineers data engineers and product teams Python or R experience is highly desirable Scientific backbone. You ll challenge unsupported assumptions define responsible limits for AI-based psychological inference and hold the line when it matters

Benefits

Build and Grow Quickly - We’re scaling fast and we trust that you’ll know best on the ground what needs to be done. You’ll have the opportunity to step into leadership early and own decisions that shape how our company grows. Fully Remote. Forever. - We’ve built Pearl with a multicultural DNA and teammates across 23 countries. We trust that the best work isn’t done behind a cubicle Unlimited PTO - We trust that you’ll get your work done and we want to create space for you to take time away with the people you care about. Global Retreats - We create space for our teammates to get to know each other as people rather than just to-do lists. We’ve shared meals laughs and sunrises across the world in places like Cancun El Nido Boracay and Siargao. Ambitious and Kind Team - We build with the most competent people we know and we maintain a low-ego no-assholes policy. We’re looking for people who are sharp kind and open to being vulnerable when it matters.