David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · INTERNAL PRODUCT
Internal Product
Psychographic Job Fit & Recruiting Intelligence Engine
A fit-scoring and recruiting-intelligence system modeling role fit, team fit, culture fit, company fit, psychographic fit, nearest-neighbor similarity,...
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,...
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, Team Fit
The Research Narrative
Strategic Problem
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...
What David Did
David modeled fit across job, role, team, culture, company, skill, motivation, and psychographic layers. Structured profiles, nearest-neighbor similarity, graph relationships, and LLM...
What Became Clearer
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...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
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
David modeled fit across job, role, team, culture, company, skill, motivation, and psychographic layers. Structured profiles, nearest-neighbor similarity, graph relationships, and LLM explanations turn raw profile data into useful decision support.
- •Modeled fit as a multidimensional system rather than a single resume-match score
- •Separated job fit, role fit, team fit, culture fit, company fit, skill fit, motivation fit, and psychographic fit into distinct scoring and explanation layers
- •Created structured profile representations for people, roles, teams, organizations, and personas using nested traits, values, thinking styles, preferences, work modes, communication patterns, and risk factors
- •Used nearest-neighbor matching to compare candidate profiles, job-role profiles, target personas, and company/team archetypes
- •Designed graph relationships between people, skills, roles, companies, values, motivations, work styles, evidence artifacts, and hiring outcomes
- •Created explainable matching narratives that identify likely strengths, mismatches, interview risks, positioning opportunities, and development themes
- •Connected psychographic matching to job dossiers and ATS intelligence so role descriptions, company signals, application questions, and hiring context could inform fit analysis
- •Designed the system to support both candidate-side positioning and recruiter-side search without overclaiming certainty
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.
Research Outcomes
Signal Quality
Improved the trustworthiness of operational security signals
Operational Clarity
Translated complex security data into clearer operating views
Stakeholder Visibility
Made technical risk and status easier to explain
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
Security Analytics
Signal investigation and event analysis
IAM / Access Control
Identity telemetry and access insights
SIEM Alert Debugging
Noise reduction and signal validation
Dashboard Development
Operational and executive views
Executive Reporting
Security data translated for leadership
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Public-Safe Evidence
Shareable insights without sensitive data
Key Deliverables
- •Multidimensional fit-scoring model
- •Structured profile schema for candidates, jobs, roles, teams, companies, and personas
- •Psychographic trait and motivation model
- •Nearest-neighbor matching workflow
- •Graph-based relationship model
- •Explainable fit narrative generator
- •Candidate positioning workflow
- •Recruiter search and shortlisting support model
Tools & Technologies
Consulting Translation
The reusable pattern is not Disney-specific: normalize fragmented security telemetry, debug low-signal alert behavior, build trusted operating views, and give leadership evidence they can act on without exposing sensitive systems.