David Wolf · Portfolio Use Case
A fit-scoring and recruiting-intelligence system modeling role fit, team fit, culture fit, company fit, psychographic fit, nearest-neighbor similarity, and explainable candidate-job matching.
Designed and built a psychographic job-fit and recruiting-intelligence engine that models job fit, role fit, team fit, culture fit, company fit, and psychographic fit using structured profiles, quantified traits, nearest-neighbor matching, graph-based relationships, and explainable fit narratives for recruiters, candidates, and hiring teams.
Client
Internal Product / Confidential Platform
Engagement Type
Internal product and research buildout
Period
2024–2026
Role
Principal Architect / Product Architect / AI Matching Systems Designer
Focus Areas
Psychographic Fit, Job Fit, Role Fit
The Context
Most recruiting systems score resumes against keywords. That is not enough. Real fit depends on role expectations, team dynamics, company operating style, motivations, communication patterns, values, and the evidence behind each claim.
The Challenge
A useful fit engine must avoid magic-score theater. It needs multidimensional scoring, confidence, caveats, evidence, missing-data handling, and responsible boundaries around sensitive attributes and employment decisions.
What I Did
The Outcome
The project creates a differentiated engine for Talent AI and Recruiter AI. It connects psychometrics, graph modeling, job intelligence, profile matching, and explainable AI into a product architecture that goes far beyond resume keyword matching.
Around
Multidimensional fit categories: job fit, role fit, team fit, culture fit, company fit, skill fit, motivation fit, and psychographic fit
Structured
Profile models for people, jobs, roles, teams, companies, and personas
To
ATS/job intelligence workflows, recruiter intelligence, candidate positioning, and personality-aware outreach
Key Deliverables
Collaboration
The work integrates product strategy, HR tech, psychometrics, graph modeling, AI matching, responsible AI constraints, recruiting workflows, and career automation into a coherent fit-intelligence system.
Client
Internal Product / Confidential Platform
Engagement Type
Internal product and research buildout
Period
2024–2026
Role
Principal Architect / Product Architect / AI Matching Systems Designer
Focus Areas
Psychographic Fit, Job Fit, Role Fit
The Context
Most recruiting systems score resumes against keywords. That is not enough. Real fit depends on role expectations, team dynamics, company operating style, motivations, communication patterns, values, and the evidence behind each claim.
The Challenge
A useful fit engine must avoid magic-score theater. It needs multidimensional scoring, confidence, caveats, evidence, missing-data handling, and responsible boundaries around sensitive attributes and employment decisions.
What I Did
The Outcome
The project creates a differentiated engine for Talent AI and Recruiter AI. It connects psychometrics, graph modeling, job intelligence, profile matching, and explainable AI into a product architecture that goes far beyond resume keyword matching.
Around
Multidimensional fit categories: job fit, role fit, team fit, culture fit, company fit, skill fit, motivation fit, and psychographic fit
Structured
Profile models for people, jobs, roles, teams, companies, and personas
To
ATS/job intelligence workflows, recruiter intelligence, candidate positioning, and personality-aware outreach
Key Deliverables
Collaboration
The work integrates product strategy, HR tech, psychometrics, graph modeling, AI matching, responsible AI constraints, recruiting workflows, and career automation into a coherent fit-intelligence system.
At a Glance
Focus Areas
Tools & Technologies
Evidence & Artifacts
Public-Safe Caveat
This case study describes internal product and research work in public-safe terms. Private profile data, proprietary scoring weights, sensitive trait inferences, source datasets, implementation details, and non-public schemas are omitted. Outputs should be framed as decision support and coaching, not automated employment selection.
David Wolf
AI Security · Product Security · Security Leadership
Based on analyzed public signals, not proof of any individual's or company's internal state.
