Senior Research Scientist, AI & Workforce Intelligence

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Research Scientist, AI & Workforce Intelligence based in the United States.

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.

Accountabilities

• Lead the continued development, architecture, and expansion of an AI-powered job analysis engine that transforms labor market data into structured, evidence-based occupational insights.
• Build, evaluate, and refine machine learning models that infer job content, cognitive complexity, occupational interests, and related attributes from unstructured text and diverse data sources.
• Apply modern NLP techniques, including embeddings, semantic search, clustering, text classification, transformer architectures, and model tuning, to improve job analysis coverage and accuracy.
• Integrate and extend established occupational frameworks, such as O*NET and ESCO, while developing approaches that capture emerging roles and evolving workforce requirements beyond existing taxonomies.
• Collaborate with product managers, software engineers, and I-O psychologists to translate scientific requirements into scalable AI capabilities across assessment interpretation, manager and team reporting, coaching content, and employee development.
• Advise cross-functional teams on appropriate AI and machine learning approaches, clarifying technical limitations, evaluation standards, uncertainty, and the role of human expertise.
• Establish and apply rigorous evaluation methods for generative AI outputs, including rubric-based assessments, groundedness checks, error analysis, regression testing, and quality assurance for generated job descriptions and work-context summaries.
• Ensure AI-supported employment and development tools meet relevant scientific, professional, ethical, and legal standards.
• Incorporate responsible AI practices, including fairness evaluation, adverse impact analysis, bias mitigation, explainability, auditability, and appropriate human review.
• Apply I-O psychology principles to norm group construction, assessment interpretation, job-relatedness evaluation, criterion relationships, and evidence-based measurement.
• Balance scientific rigor with practical product delivery, making informed trade-offs between accuracy, interpretability, automation, maintainability, and operational constraints.
• Contribute to the development of new assessments that use emerging technologies to support richer, more secure, and effective evaluation of individuals.
• Establish collaborative relationships across scientific and product teams, develop technical roadmaps, and deliver measurable improvements to model performance and platform capabilities.

Requirements:

• 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 …