About

About Me

I’m a psychologist, psychometrician, product strategist, and builder of measurement-driven systems. Not a developer. Not a pure academic. The gap between those worlds is where the interesting problems live.

My work spans academia, psychometrics, product, growth engineering, and AI — not because the profile drifted, but because the same underlying skill set runs through all of it: turning vague human problems into measurable, testable systems. The through-line is measurement: if you can’t quantify it rigorously, you can’t build on it — and making decisions legible, comparable, improvable, and harder to fake is what I care about most.

What I Actually Do

People come to me when they need to design assessment systems, translate expert knowledge into product logic, build matching or scoring models, structure AI-assisted workflows, or turn a complex domain into a scalable tool or growth system. The domains include psychometric assessment, hiring, talent development, workforce development, legal tech, health interpretation, and programmatic growth. Custom psychometric systems for EPAM and gr8.tech. Workforce development academies with Relativity. Public career assessment for HSE Ireland. Programmatic and product-led growth for Gyfted and Prawomat.

I’m especially useful when the raw material is expert knowledge, complicated rules, or fuzzy human judgment — and the task is to turn it into a product, workflow, or measurable engine. The problems I’m drawn to involve hidden human variables, messy real-world decisions, unclear success criteria, or domains where people rely too much on intuition and weak proxies.

A large part of my value is translation: between researchers and product teams, between domain experts and engineers, between conceptual models and usable systems. My role usually begins before code — framing the problem, designing the model, defining measurement choices, specifying UX logic, and setting evaluation criteria. Spec-driven development is my core method: define the structure, logic, assumptions, edge cases, and validation criteria before implementation starts.

Academic Career

I’ve been an adjunct at SWPS University’s Department of Psychological Research Methodology since 2006. I directed the Experimental Psychology Laboratory from 2010 to 2024. I teach methodology, psychometrics, data analysis, and R programming — the courses where students learn that their intuitions need evidence behind them.

My PhD in Psychology (Cognitive Science, SWPS, 2024) focused on the cognitive accessibility of observable and unobservable properties in thinking about self and others — essentially, how people think about visible versus hidden traits in themselves and others, and why some aspects of a person are cognitively easier to access than others. That question turns out to matter a lot for assessment design, profiling, and feedback systems: what you can measure depends on what’s psychologically accessible, and to whom.

Postgraduate work in Advanced Statistical Methods at the London School of Economics (2018) gave me multilevel modeling, latent class analysis, and structural equation modeling — tools I use in both research and product work. What academia gave me that I still use every day: construct clarity, skepticism toward weak measures, respect for validation, comfort with uncertainty, and a habit of testing claims before scaling them.

Research

My research sits at the intersection of cognitive accessibility, self-concept formation, and intergroup behavior. I study how people construct mental representations of themselves and others, what makes certain properties cognitively accessible, and how that accessibility shapes judgment. Adjacent lines cover collective narcissism, revenge motivation, and moral emotions. Alongside the substantive psychology, I’ve worked on methodological questions — how to analyze complex behavioral data and draw defensible conclusions from imperfect signals.

Published in Journal of Personality and Social Psychology, Self and Identity, PLoS ONE, Aggressive Behavior, Personality and Individual Differences, and Journal of Social Psychology, among others. Research funded by NCN (Maestro, OPUS) and NCBR grants. I treat negative results as data, not failures — they narrow the hypothesis space. I care as much about disproving weak ideas as proving promising ones.

The research isn’t decorative background. Self-concept, trait accessibility, and social perception feed directly into how I design profiling, assessment, matching, and feedback systems commercially. The science and the product work are the same discipline applied at different distances from the user.

Building Products

At Gyfted / Human Exponent, I design assessment logic, scoring models, matching systems, custom client assessments, and the decision layer that turns measured traits and competencies into usable outputs for hiring and development. At Nerds.family, I built Academy 360 — a full-cycle workforce development model that simultaneously develops participants, generates evidence of capability, and gives employers access to a more credible talent pipeline. At Digital Savages / SEO Savages, I build programmatic and AI-driven growth systems — and SEO belongs on the same page as psychometrics because it’s the same work underneath: measurement, information architecture, system design, experimentation, and scaling high-signal assets.

The thread across all of them: measurement matters, and the gap between “we think this works” and “we measured that this works” is where most products fail.

How I Work

Systems first. Evidence always. Then build.

I use AI as a technical co-founder — not a coding assistant, but a thinking partner that compresses the distance between concept and implementation: clarifying architectures, stress-testing ideas, drafting specs, reasoning through edge cases, and accelerating product thinking without outsourcing judgment. I think in systems, data, and methodology. Everything gets front-loaded: the structure, the logic, the assumptions, the validation criteria — before implementation starts.

I’m comfortable in both theory-heavy and execution-heavy environments, which is why I can move between university research, venture-backed products, startup growth systems, and applied client work without it feeling fragmented. I care about clarity, seriousness, systems that survive contact with reality, and work that earns its claims.

Working With Me

I usually enter when a founder, team, or organization has a high-stakes problem with fuzzy logic underneath it: an assessment to build, a matching problem to solve, a product to structure, a domain to operationalize, or an evidence question to clean up.

If you’re building products, measurement systems, talent infrastructure, or AI-assisted workflows that need more rigor than they currently have — get in touch. I’ll tell you honestly whether I can help.