Behavioral Science Specialist - Supervising Associate

The Behavioral Scientist will apply advanced behavioral science, psychometrics, and quantitative and qualitative research methods to design, analyze, and interpret complex talent and employee experience data. The role is responsible for developing scientifically rigorous insights that inform strategic talent decisions, workforce interventions, and organizational effectiveness initiatives. This position requires deep expertise in organizational research methodology, predictive and prescriptive modeling, and statistical programming. The Behavioral Scientist partners closely with Talent Insights and Analytics, Employee Listening, and senior Talent stakeholders to translate research findings into practical, evidence‑based recommendations. The role contributes to advancing the firm’s analytical maturity by embedding strong scientific standards, scalable methodologies, and advanced modeling approaches into talent analytics and decision‑making.

Essential Functions of the Job

• Design and conduct applied organizational research using robust quantitative methodologies, including experimental, quasi‑experimental, and longitudinal designs.
• Lead advanced analysis of employee listening, experience, and talent data using statistical programming languages (SQL, R, and/or Python).
• Apply advanced predictive and prescriptive modeling techniques (e.g., regression, multilevel models, machine learning, optimization approaches) to address complex talent and workforce questions.
• Develop and validate psychometric models, including scale development, factor analysis, reliability and validity assessment, and measurement invariance testing.
• Integrate multiple data sources (survey, experience, talent, and operational data) to generate holistic and scientifically defensible insights.
• Translate complex analytical and research outputs into clear narratives, recommendations, and decision‑support materials for senior Talent and business stakeholders.
• Support the design and evaluation of talent interventions by defining success metrics, analytic approaches, and evidence thresholds.
• Contribute to the development of standardized research and analytics frameworks, ensuring consistency, rigor, and scalability across Talent Insights and Analytics.
• Partner with Talent IT, data engineering teams, and external vendors to ensure appropriate data structures, access, and analytical readiness.
• Monitor emerging research, analytical techniques, and best practices in behavioral science, psychometrics, and people analytics to continuously enhance internal capabilities.

Analytical/Decision Making Responsibilities

• Apply expert judgment in selecting appropriate research designs, statistical methods, and modeling approaches based on business context and data constraints.
• Make recommendations on analytic strategies, modeling techniques, and interpretation standards to ensure scientific rigor and decision relevance.
• Evaluate the strength of evidence supporting findings and clearly communicate limitations, assumptions, and confidence levels.
• Support decisions related to prioritization, design, and continuous improvement of Talent Insights and Analytics research and modeling initiatives.
• Influence stakeholder decision‑making by synthesizing data, research findings, and behavioral science principles into actionable guidance.

Supervision Responsibilities

• Act as a subject matter expert and thought partner within Talent Insights and Analytics.
• Provide informal coaching and technical guidance to colleagues on research design, analytics, and interpretation.
• Collaborate closely with cross‑functional teams to ensure analytical quality, consistency, and impact.
• Build trusted relationships through credibility, rigor, and constructive challenge.
• Ability to develop people, skills in coaching, mentoring, and learning on the job.
• Effectiveness in building trust, respect, and cooperation among teams.

Knowledge and Skills Requirements

• Deep expertise in behavioral science, with strong grounding in Industrial and Organizational Psychology or a closely related discipline.
• Advanced knowledge of organizational research methodology, including experimental design, causal inference, and applied statistical modeling.
• Strong command of psychometrics, including scale construction, validation, and measurement theory.
• Advanced analytical skills using SQL, R, and/or Python for data extraction, manipulation, modeling, and analysis.
• Demonstrated expertise in predictive and prescriptive analytics, including advanced statistical and machine learning techniques.
• Strong data storytelling and visualization skills to communicate complex findings effectively to non‑technical audiences.
• Strong business acumen and ability to contextualize research insights within broader talent and organizational strategies.
• Ability to operate independently on complex analytical problems while collaborating effectively in cross‑functional, global teams.
• High tolerance for ambiguity and ability to apply scientific rigor in fast‑paced, evolving environments.
• Strong written and verbal communication skills, with a global and culturally aware mindset.

Other Requirements

• Due to the global nature of the role, flexibility to work alternative hours may be required.
• Excellent written and verbal English communication skills are required.

Job Requirements

• Education: Ph.D. strongly preferred in Industrial and Organizational Psychology, Organizational Psychology, Behavioural Science, Statistics, or a closely related quantitative discipline. Master’s degree with significant applied research experience may be considered.
• Certification Requirements: Professional certifications or active engagement in relevant professional or academic communities preferred but not required.
• Experience: Demonstrated experience conducting applied organizational research in a business, consulting, or applied research environment. Experience applying advanced statistical, predictive, and prescriptive modeling techniques to real‑world organizational problems. Hands‑on experience with psychometric analysis and measurement development in organizational contexts. Experience working with large‑scale employee listening or experience datasets. Proven ability to translate complex research findings into practical, business‑relevant insights. Experience collaborating with Talent, HR, or People teams to shape evidence‑based decisions. Experience working in global, matrixed, and fast‑paced environments. Experience partnering with data engineering teams or external vendors to enable advanced analytics.