Senior Research Scientist, AI & Workforce Intelligence

Wonderlic•Vernon Hills, IL

17h•Remote

About The Position

Wonderlic is seeking a Senior Research Scientist to sit at the precise intersection of I-O psychology and machine learning. This role is for someone who has spent time building real systems with real data and cares deeply about what those systems are predicting. The ideal candidate has genuinely lived in both worlds (I-O psychology and ML engineering) and is ready to own the problems that live between them. Wonderlic has developed the Jobs Engine, a machine learning system for job analysis at scale, and is now refining and expanding this work. The Senior Research Scientist will own the continued improvement of the Jobs Engine and serve as an expert on how AI should be applied to employee selection and development. This role will set the scientific and technical direction for how Wonderlic understands work at scale and translates assessment science into AI-powered insights, directly impacting hiring decisions, development plans, and coaching conversations for millions of users.

Requirements

Applied NLP and ML engineering skills: embeddings, semantic search, clustering, text classification, transformer architectures, model tuning and evaluation on unstructured data.
Occupational data modeling: job titles, task statements, skills, competencies, credentials, job families, seniority levels, title normalization, job similarity, role differentiation, and occupational frameworks such as ONET and ESCO.
Responsible AI judgment in employment contexts: fairness, explainability, auditability, bias mitigation, human review, and legal and ethical considerations in AI-supported selection and employee development systems.
Generative AI evaluation skills: rubric-based review, groundedness checks, error analysis, regression testing, and evaluation of LLM-generated job descriptions, work-context summaries, and assessment result contextualization.
Product judgment for applied ML systems: balancing accuracy, explainability, automation, expert review, user input, maintainability, uncertainty, and job-specific nuance.
Working fluency with assessment and I/O concepts: job relatedness, criterion relationships, adverse impact, norm groups, assessment profiles, and score interpretation.
Ability to own ambiguous, high-complexity problems: framing underspecified problems, challenging weak assumptions, learning domain constraints quickly, and driving durable solutions in a small-company environment.
Graduate degree in I-O Psychology, Organizational Psychology, Organizational Development, or closely related field (quantitative focus strongly preferred); doctoral degree a plus.
Demonstrated ML engineering experience with shipped, production-grade systems.
Experience applying modern NLP methods to behavioral, assessment-based, or labor market data.
Track record of work that had to be both technically sound and legally/professionally defensible.
Minimum 3 years of applied industry experience; 5+ years preferred.
Experience at the intersection of I-O science and algorithmic fairness strongly preferred.
Familiarity with occupational taxonomies, vocational interests, or cognitive ability frameworks a significant plus.

Nice To Haves

Doctoral degree in I-O Psychology, Organizational Psychology, Organizational Development, or closely related field.

Responsibilities

Lead the continued development of the Jobs Engine, owning its architecture, integrity, and expansion. This involves ingesting labor market data to extrapolate cognitive complexity ratings, norm groups, and occupational interest profiles for thousands of jobs.
Build and refine models that make inferences about work content from unstructured text, leveraging and expanding upon existing occupational taxonomies.
Ensure all outputs are scientifically defensible and scalable as the nature of work evolves.
Act as an expert on AI implementation across the organization, partnering with product managers, engineers, and I-O psychologists to translate scientific requirements into AI-powered systems.
Advise on the best approaches for specific implementation challenges such as assessment interpretation, reporting, and coaching content.
Serve as an internal resource on the capabilities and limitations of ML and AI in assessment and organizational contexts.
Contribute to Wonderlic’s external scientific credibility.
Drive scientific rigor by applying I-O psychology principles (adverse impact consideration, norm group construction, evidence-based evaluation) to all systems.
Ensure Wonderlic’s AI products meet professional and legal standards for selection and development tools.
Push back when speed is prioritized over defensibility and find pragmatic paths forward.