Senior Research Scientist, AI & Workforce Intelligence

About the Company

Wonderlic is focused on fair, predictive science that helps people find work where they can succeed and grow. The company combines assessment expertise with industrial-organizational psychology, machine learning, and artificial intelligence to generate evidence-based insights that support better hiring and development decisions.

The organization supports a balanced work style with a four-day week, the ability to work from anywhere in the United States, generous paid time off, a paid company shutdown from 12/24 through 1/1, and benefits that include medical, dental, vision, a 401(k) with matching, and paid leave for new parents.

What Makes the Team Different

• Scientific rigor is central to how assessments are designed and evaluated, with methods built to measure potential and fit accurately.
• The company emphasizes ongoing innovation by adopting modern techniques and technologies to keep its talent assessment tools current.
• Its products are meant to create practical impact by combining I-O psychology with hiring and retention needs that matter over the long term.

Role Overview

Wonderlic is hiring a Senior Research Scientist who works at the overlap of I-O psychology and machine learning. The role is intended for someone who has already built real production systems with real data and cares deeply about the meaning and quality of the predictions those systems produce. The person in this role will own the continued evolution of the Jobs Engine, a machine learning system that performs job analysis at scale by ingesting labor market data and turning it into structured insights for thousands of roles.

This role also serves as a company-wide authority on how AI should be applied to employee selection and employee development. It requires someone who can think like both a scientist and a production ML builder, and who can move between technical implementation and scientific defensibility without losing either perspective.

What You Will Do

• Lead the ongoing development of the Jobs Engine by owning its architecture, reliability, and expansion.
• Improve systems that infer work content from unstructured text and generate scalable job analysis outputs.
• Use labor market and occupational sources such as O*NET, LinkedIn, Indeed, and other datasets to estimate cognitive complexity ratings, norm groups, and occupational interest profiles.
• Extend existing occupational taxonomies such as O*NET and ESCO where needed, while also going beyond them when the problem requires it.
• Ensure that all outputs remain scientifically sound, defensible, and scalable as work roles continue to change.
• Partner with product managers, engineers, and I-O psychologists to convert research needs into AI-enabled product features.
• Advise teams on the right AI approach for assessment interpretation, manager and team reporting, coaching content, and similar use cases.
• Help define what strong quality looks like before build-out and after launch.
• Serve as an internal guide on what machine learning and AI can realistically do in hiring and development contexts.
• Apply I-O psychology standards such as adverse impact review, norm group construction, and evidence-based evaluation to the systems you build.
• Protect legal and professional defensibility by pushing back when speed threatens quality, while still finding practical paths to shipping.
• Support the continued integration of AI across the platform and help shape new assessments that use emerging technologies for richer and more secure evaluation.

Success Measures

Within the first six months, you are expected to understand the Jobs Engine architecture deeply and deliver at least one meaningful improvement to coverage, accuracy, or scientific defensibility. You should also establish productive working relationships with the I-O and Product teams and help define the roadmap for broader AI integration.

By the one-year mark, you should have materially improved the Jobs Engine’s reach and quality, with clear gains in coverage and inference accuracy. You should also be the person colleagues rely on for complex problems where ML and I-O science meet.

Long term, success looks like a defensible job-analysis system that can scale across millions of roles using many types of input, AI features that stand up to scientific and professional scrutiny, and new assessment methods enabled by emerging technology.

Required Background and Skills

• Strong applied NLP and ML engineering capability, including embeddings, semantic search, clustering, text classification, transformer models, tuning, and evaluation on messy unstructured data.
• Experience modeling occupational data such as titles, task statements, skills, competencies, credentials, job families, seniority levels, title normalization, and job similarity.
• Practical understanding of occupational frameworks such as O*NET and ESCO.
• Sound judgment around responsible AI in employment settings, including fairness, explainability, auditability, bias mitigation, human review, and legal and ethical concerns.
• Ability to evaluate generative AI outputs using rubrics, groundedness checks, regression testing, and error analysis.
• Strong product sense for applied ML systems, with the ability to balance accuracy, explainability, automation, user input, expert review, maintainability, and uncertainty.
• Working fluency in assessment and I-O psychology concepts such as job relatedness, criterion relationships, adverse impact, norm groups, assessment profiles, and score interpretation.
• Comfort handling ambiguous, high-complexity problems and turning underspecified goals into durable solutions in a small-company environment.

Mindset We Value

• You care deeply about work, why it matters, and how people fit into it.
• You understand that shipping well is sometimes better than waiting for perfect.
• You can simultaneously think about the impact on a real person and the system that produced the output.
• You are interested in both employee selection and employee development, not just one side of the problem.
• You do well in scrappy environments where ownership is high, constraints are real, and creative problem-solving is essential.

Qualifications

• Graduate degree in I-O Psychology, Organizational Psychology, Organizational Development, or a closely related discipline, with a quantitative focus strongly preferred.
• Doctoral education is an advantage.
• Proven experience building and shipping production-grade ML systems, beyond research or coursework.
• Background applying modern NLP methods to behavioral, assessment, or labor market data.
• Work history showing solutions that were both technically strong and legally or professionally defensible.
• At least 3 years of applied industry experience; 5 or more years is preferred.
• Prior work at the intersection of I-O science and algorithmic fairness is strongly preferred.
• Exposure to occupational taxonomies, vocational interests, or cognitive ability frameworks is a strong plus.

Compensation and Benefits

The salary range for this role is $95,000 to $110,000, depending on experience and expertise.

Benefits include medical, dental, and vision coverage, a 401(k) with matching, paid new parent leave, generous PTO, and a paid shutdown from December 24 through January 1. The position also supports a four-day work week and remote work from anywhere in the United States.

Equal Opportunity and Accommodation

Wonderlic encourages applicants from underrepresented and nontraditional backgrounds to apply, even if they do not meet every listed qualification. The company is committed to equal employment opportunity and affirmative action and does not discriminate based on race, color, religion, gender, gender identity, pregnancy status, national origin, sexual orientation, marital status, disability, genetic information, age, parental status, military or veteran status, or any other legally protected factor.

Reasonable accommodations are available for the application or interview process, essential job functions, and employment benefits. Candidates may request accommodation by contacting [email protected].

Important Notice

This summary is not a complete list of duties or responsibilities. The scope of the role may change, and additional duties may be assigned with or without prior notice.

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