Senior Research Scientist, AI & Workforce Intelligence

We are seeking a Senior Research Scientist who combines deep knowledge of industrial-organizational (I-O) psychology with hands-on machine learning engineering expertise to transform how organizations understand jobs, talent, and workforce potential. In this fully remote role, you will lead the evolution of an AI-powered job analysis platform that processes labor market data to generate evidence-based insights across thousands of occupations. You will develop and refine advanced NLP and machine learning models while ensuring their outputs remain accurate, explainable, fair, and scientifically defensible. Working closely with product managers, engineers, and assessment scientists, you will shape how AI supports employee selection, professional development, coaching, and organizational decision-making. This position offers meaningful technical ownership, opportunities to influence AI strategy, and the chance to solve complex problems at the intersection of people science and production-grade technology. You will help build responsible AI solutions that improve how individuals and organizations understand work, performance, and potential.

• Graduate degree in Industrial-Organizational Psychology, Organizational Psychology, Organizational Development, or a closely related field. A strong quantitative focus is preferred, and a doctoral degree is an advantage.

• At least three years of applied industry experience, with five or more years preferred.

• Demonstrated machine learning engineering experience delivering production-grade systems, rather than experience limited to academic research or coursework.

• Practical experience applying modern NLP and machine learning techniques to behavioral, assessment-related, occupational, or labor market data.

• Strong technical skills in embeddings, semantic search, clustering, text classification, transformer architectures, model tuning, and model evaluation.

• Experience modeling occupational data, including job titles, tasks, skills, competencies, credentials, job families, seniority levels, title normalization, job similarity, and role differentiation.

• Familiarity with occupational taxonomies such as O*NET and ESCO, vocational interests, cognitive ability frameworks, and job analysis methodologies is highly desirable.

• Working knowledge of I-O psychology and assessment science, including job-relatedness, criterion relationships, adverse impact, norm groups, assessment profiles, and score interpretation.

• Demonstrated understanding of responsible AI in employment contexts, including algorithmic fairness, explainability, auditability, bias mitigation, and legal and ethical considerations.

• Experience evaluating generative AI systems through structured rubrics, groundedness checks, error analysis, regression testing, or similar approaches.

• A track record of delivering solutions that are both technically robust and professionally or legally defensible.

• Strong product judgment and the ability to balance scientific validity, model performance, user needs, uncertainty, expert review, and real-world implementation constraints.

• Excellent analytical and problem-solving skills, with the ability to frame ambiguous problems, challenge assumptions, learn unfamiliar domain constraints, and develop durable solutions.

• Strong communication and collaboration skills, including the ability to explain complex scientific and technical concepts to multidisciplinary audiences.

• A pragmatic, adaptable mindset and the ability to take ownership in a small-team environment where creativity, independent thinking, and hands-on execution are essential.

• Genuine interest in understanding how people work, what drives performance, and how evidence-based technology can improve employee selection and development.

• Must be based in the United States and comfortable working remotely.