Senior Research Scientist, AI & Workforce Intelligence - Wonderlic

About Us

Work a four-day week from anywhere for a company where people genuinely believe in what they do. Wonderlic leads the way in fair, predictive science to create a world where everyone finds and thrives in their best job—and that starts with you. We expertly combine our science-based assessment expertise with I-O psychology, machine learning, and artificial intelligence to deliver evidence-based insights that empower smarter employment decisions.

Wonderlic champions progressive, sustainable workplace practices:

• Work from anywhere in the United States
• Four-day work week (32 hours)
• Generous PTO plus a paid company shutdown from 12/24 to 1/1
• Comprehensive benefits including medical, dental, vision, 401(k) with matching, and paid new parent leave

Overview

Wonderlic is seeking a Senior Research Scientist to sit at the precise intersection of I-O psychology and machine learning—someone who has built production systems with real data and cares deeply about what those systems predict. We are looking for someone who has genuinely lived in both worlds and is ready to own the problems between them.

You will own the continued improvement of our Jobs Engine, the machine learning system that ingests labor market data and produces job analysis at scale across thousands of roles, and serve as one of the organization’s experts on applying AI to employee selection and development.

Your Impact

As Senior Research Scientist, you will set the scientific and technical direction for how Wonderlic understands work at scale and translates assessment science into AI-powered insights. Your work will directly determine the quality, defensibility, and reach of inferences driving hiring decisions, development plans, and coaching conversations for millions of users.

What You’ll Do

• Lead the Continued Development of the Jobs Engine: Own the architecture, integrity, and continuous expansion of Wonderlic’s AI job analysis system, which ingests labor market data from ONET, LinkedIn, Indeed, and other sources to extrapolate cognitive complexity ratings, norm groups, and occupational interest profiles. Build and refine models that infer work content from unstructured text while expanding beyond traditional taxonomies (e.g., ONET, ESCO).
• Act as an Expert on AI Implementation: Partner with product managers, engineers, and I-O psychologists to translate scientific requirements into AI-powered systems. Advise on approaches suited to assessment interpretation, manager/team reporting, and coaching content. Guide teams on what ML and AI can and cannot do in organizational contexts.
• Drive Scientific Rigor: Apply core I-O psychology principles—including adverse impact consideration, norm group construction, and evidence-based evaluation standards—to every system built. Ensure all AI products meet professional and legal standards for selection and development tools.

What We’re Looking For

Skills & Capabilities

• Applied NLP and ML engineering: Embeddings, semantic search, clustering, text classification, transformer architectures, model tuning, and evaluation on messy, unstructured data.
• Occupational data modeling: Deep understanding of job titles, task statements, skills, competencies, credentials, title normalization, role differentiation, and occupational frameworks.
• Responsible AI in employment contexts: Experience evaluating fairness, explainability, auditability, bias mitigation, human review, and ethical considerations in AI-supported talent systems.
• Generative AI evaluation: Rubric-based review, groundedness checks, error analysis, regression testing, and evaluation of LLM-generated job descriptions and assessment contextualization.
• Product judgment: Proven ability to balance accuracy, explainability, automation, expert review, user input, and uncertainty.
• Fluency with I-O concepts: Job relatedness, criterion relationships, adverse impact, norm groups, assessment profiles, and score interpretation.
• Problem ownership: Capacity to frame underspecified problems, challenge weak assumptions, and drive durable solutions.

Qualifications

• Graduate degree in I-O Psychology, Organizational Psychology, Organizational Development, or a closely related quantitative field (doctoral degree a plus)
• Demonstrated ML engineering experience shipping production-grade systems
• Experience applying modern NLP methods to behavioral, assessment-based, or labor market data
• Track record of work that is 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 is a significant plus