Senior Research Scientist, AI & Workforce Intelligence

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 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. Benefits: Annual base salary: $130,000–$150,000, depending on experience and expertise. Fully remote work: Work from anywhere in the United States. Four-day workweek: Full-time schedule of 32 hours across four days. Paid time off: Generous PTO and paid holidays. Year-end company shutdown: Paid time off from December 24 through January 1. Healthcare coverage: Medical, dental, and vision insurance. Retirement benefits: 401(k) plan with company matching. Paid parental leave: 10 weeks of paid leave for new parents. Wellness support: Dedicated wellness benefit to support personal well-being. Meaningful scientific impact: Help advance evidence-based employment assessments and responsible AI applications that influence workforce decisions. Technical and scientific ownership: Shape the direction of applied AI systems while collaborating with experts across machine learning, product development, and I-O psychology. Innovation-focused environment: Work on complex, real-world challenges at the intersection of workforce intelligence, assessment science, and emerging AI technologies.