Senior Research Scientist, AI & Workforce Intelligence

Wonderlic•Vernon Hills, IL

1d•$95,000 - $110,000•Remote

About The Position

Wonderlic is seeking a Senior Research Scientist to bridge the gap between I-O psychology and machine learning. This role is for someone who has experience building real systems with real data and is passionate about what those systems predict. The ideal candidate has lived in both worlds and is ready to own the problems that exist between them. Wonderlic has a strong track record in applied AI/ML, including developing the Jobs Engine and winning the SIOP Machine Learning Competition. This role will involve owning the continued improvement of the Jobs Engine, a system that analyzes labor market data to provide job insights, and serving as an expert on applying AI to employee selection and development.

Requirements

Graduate degree in I-O Psychology, Organizational Psychology, Organizational Development, or a 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 was both technically sound and legally/professionally defensible.
Minimum 3 years of applied industry experience; 5+ years preferred.
Applied NLP and ML engineering skills: embeddings, semantic search, clustering, text classification, transformer architectures, model tuning and evaluation, on potentially messy, 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.
Mindset: Cares about work, meaning, thriving, and measurement of fit.
Mindset: Has a healthy relationship with 'good enough' and knows the line between perfect and shipped.
Mindset: Can hold both the I-O science question and the production ML question simultaneously.
Mindset: Genuinely curious about problems in employee selection and development.
Mindset: Thrives in an environment requiring creativity and scrappiness, with hard problems, a small team, many constraints, and real ownership.

Nice To Haves

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

Responsibilities

Lead the continued development of the Jobs Engine, owning its architecture, integrity, and expansion. This includes ingesting labor market data to extrapolate cognitive complexity ratings, norm groups, and occupational interest profiles for thousands of jobs.
Build and refine models that infer work content from unstructured text, leveraging and expanding upon existing occupational taxonomies like ONET and ESCO.
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.
Translate scientific requirements into AI-powered systems and ensure they meet required standards.
Advise on the best approaches for specific implementation challenges, such as assessment interpretation, manager and team 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, including adverse impact consideration, norm group construction, and evidence-based evaluation standards.
Ensure 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.
Develop new assessments that leverage emerging technologies for richer and more secure individual evaluation.