[Hiring] Senior Research Scientist @Wonderlic

Role Description

Wonderlic is seeking a Senior Research Scientist to sit at the precise intersection of I-O psychology and machine learning - someone who has spent time building real systems with real data who cares deeply about what those systems are predicting. This is not a role for an ML engineer who finds people data interesting as a side project, or for an I-O psychologist who has learned to code. We’re looking for someone who has genuinely lived in both worlds and is ready to own the problems that live between them.

Your Impact:

• Set the scientific and technical direction for how Wonderlic understands work at scale and translates assessment science into AI-powered insight.
• Directly determine the quality, defensibility, and reach of the inferences our platform makes about jobs and people.
• Hold both the I-O science question and the production ML question in the same breath, and answer both.

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.
  • Build and refine the models that make inferences about work content from unstructured text.
  • Ensure all outputs are scientifically defensible and scalable as the nature of work evolves.
• Act as an Expert on AI Implementation Across the Organization:
  • Partner with product managers, engineers, and I-O psychologists to translate scientific requirements into AI-powered systems.
  • Advise on which approaches are best suited to specific implementation challenges.
  • Serve as an internal resource on what ML and AI can and cannot do in assessment and organizational contexts.
• Drive Scientific Rigor:
  • Apply I-O psychology principles 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.

What Success Looks Like

• In your first six months, you will have a deep understanding of the jobs engine architecture and shipped at least one meaningful improvement.
• Within a year, you will have materially expanded the jobs engine’s reach and accuracy.
• The long-term marker of success is a jobs engine that provides defensible, scaled job analysis inference across millions of roles.

What We’re Looking For

Skills & Capabilities:

• Applied NLP and ML engineering skills.
• Occupational data modeling.
• Responsible AI judgment in employment contexts.
• Generative AI evaluation skills.
• Product judgment for applied ML systems.
• Working fluency with assessment and I/O concepts.
• Ability to own ambiguous, high-complexity problems.

Mindset:

• You care about work - what makes it meaningful, who thrives in it, how to measure fit.
• You have a healthy relationship with “good enough”.
• You can hold both worlds simultaneously.
• You are genuinely curious about the problems that exist for both employee selection and development.
• You thrive in an environment that requires creativity and scrappiness.

Qualifications

• 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.

Compensation

$130,000 to $150,000 based on experience and expertise.

Our Policy

Wonderlic is proud to be an equal employment opportunity/affirmative action employer. Here, diversity is valued and celebrated, and this is what makes us such a successful team.