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Lead Workforce Scientist (P5) | Focus Area: Strategic Workforce Planning & Organizational Effectiveness
Senior Individual Contributor | Workforce Intelligence Data Science, Research & Advisory
POSITION SUMMARY
We are seeking an applied workforce scientist who combines advanced quantitative methods, workforce planning expertise, and business acumen to help leaders understand how strategy, technology, and operating-model choices will shape future workforce needs. The role is designed for complex workforce phenomena and strategic uncertainty, not only defined planning needs. The Lead Workforce Scientist peers around corners, surfacing the risk, opportunity, or question that leaders did not yet know existed and revealing workforce implications before they become visible through standard planning processes.
The Lead Workforce Scientist develops advanced forecasting, scenario, simulation, predictive models, and analytical methodologies that translate business strategy into quantified workforce choices and organizational recommendations. These capabilities are deliberately built to test uncertainty, detect risks and opportunities the organization was not explicitly looking for, and expand what Workforce Intelligence is capable of knowing about the future. The role integrates HR, financial, operational, and external labor-market evidence to evaluate alternative futures, including how AI and automation may reshape work, skills, roles, and organization structures. The ideal candidate is equally energized by the workforce problem and the analytical challenge, and can turn complex model outputs into clear decisions on workforce investment, organization design, and transformation.
Forecasting, Scenario & Predictive Modeling | Analytical Data Development & Reproducibility
• Build workforce demand and supply forecasting models using, but not limited to, time-series, regression, and machine learning methods that link headcount, skills, capacity, and cost to business drivers.
• Develop scenario and simulation models, such as Monte Carlo, Markov, and stock-and-flow approaches, to test strategic uncertainty, expose hidden dependencies, and quantify future workforce needs under alternative business strategies.
• Build and validate predictive models for talent flows, capacity, capability gaps, and other workforce outcomes, assessing accuracy, stability, and fairness.
• Apply statistical and causal methods to organizational effectiveness questions such as spans and layers, operating models, productivity, and capacity.
• Use Python or R and SQL to integrate and analyze HRIS, finance, operational, skills, and external labor-market data at enterprise scale.
• Develop fit-for-purpose analytical datasets and derived measures that support forecasting, simulations, organization analysis, and workforce decisions.
• Work in cloud data platforms such as Databricks, using version control, documentation, and reproducible methods so analyses and models can be reviewed, reused, and maintained.
• Partner with HR data platform, finance, and technology teams to define data requirements, quality expectations, and sustainable access for workforce planning analyses.
AI & Future of Work Modeling | Domain Leadership & Advisor y
• Quantify the workforce implications of AI, automation, and emerging technologies by modeling task-level exposure and potential changes across…