[Remote] Senior Research Scientist, AI & Workforce Intelligence - Remote, 4-Day Work Week

Note: The job is a remote job and is open to candidates in USA. Wonderlic is seeking a Senior Research Scientist to guide the scientific and technical direction of its AI-powered job analysis and workforce insight systems. The role owns improvements to the Jobs Engine, advises teams on AI implementation, and ensures products meet scientific, professional, and legal standards.

Responsibilities

• Own the architecture, integrity, and continuous expansion of Wonderlic’s AI job analysis system, which ingests labor market data from O*NET, LinkedIn, Indeed, and other sources to extrapolate cognitive complexity ratings, norm groups, and occupational interest profiles for thousands of jobs. Build and refine the models that make inferences about work content from unstructured text. Leverage existing occupational taxonomies (e.g., O*NET, ESCO) where appropriate but also expand beyond them. Ensure all outputs are scientifically defensible and scalable as the nature of work evolves
• Partner with product managers, engineers, and I-O psychologists to translate scientific requirements into AI-powered systems and ensure those systems meet the standards the work demands. Advise on which approaches are best suited to specific implementation challenges - assessment interpretation, manager and teams reporting, coaching content - and help teams understand what good looks like before they build and after they ship. Serve as an internal resource on what ML and AI can and cannot do in assessment and organizational contexts, and contribute to Wonderlic’s external scientific credibility
• Apply I-O psychology principles - adverse impact consideration, norm group construction, and evidence-based evaluation standards - to every system you build. Ensure that Wonderlic’s AI products meet professional and legal standards for selection and development tools. Push back when speed is being prioritized at the expense of defensibility, and find pragmatic paths forward when theoretical purity would prevent shipping

Skills

• Applied NLP and ML engineering skills: embeddings, semantic search, clustering, text classification, transformer architectures, model tuning and evaluation, all 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 O*NET 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
• Demonstrated ML engineering experience with shipped, production-grade systems - not just research or coursework
• 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
• Quantitative focus strongly preferred
• Doctoral degree a plus
• 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

Benefits

• Remote-first; work from anywhere in the United States.
• Full-time, 32-hour workweek across four days.
• Generous PTO, paid holidays, and a paid company shutdown from December 24 through January 1.
• Medical, dental, and vision coverage.
• A 401(k) with company matching.
• 10 weeks of paid new parent leave.
• A dedicated wellness benefit to support your overall well-being.

Company Overview

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