Lead Workforce Scientist

Position summary

Modeling and analytical data

• Build workforce demand and supply forecasts using methods such as time-series, regression, and machine learning, linking headcount, skills, capacity, and cost to business drivers.
• Develop scenario and simulation models, including Monte Carlo, Markov, and stock-and-flow approaches, to test strategic uncertainty and quantify future workforce needs.
• Build and validate predictive models for talent flows, capacity, capability gaps, and other workforce outcomes; assess accuracy, stability, and fairness.
• Apply statistical and causal methods to organizational effectiveness topics such as spans and layers, operating models, productivity, and capacity.
• Use Python or R and SQL to integrate and analyze enterprise-scale HRIS, finance, operational, skills, and labor-market data.
• Develop fit-for-purpose analytical datasets and derived measures for forecasting, simulation, organization analysis, and workforce decisions.
• Use cloud data platforms such as Databricks, version control, documentation, and reproducible methods to make analyses reviewable and maintainable.
• Partner with HR data platform, finance, and technology teams on data requirements, quality expectations, and sustainable access.

Future of work and advisory

• Model workforce implications of AI, automation, and emerging technologies across tasks, roles, skills, capacity, and cost.
• Develop scenarios distinguishing human, automated, and AI-augmented work and clarify their choices, assumptions, and uncertainties.
• Evaluate external evidence and labor-market signals to inform assumptions about future skills, talent availability, and workforce transformation.
• Partner with technology and product teams on AI-enabled forecasting and decision support, applying responsible AI standards.
• Serve as an enterprise subject-matter leader in strategic workforce planning, future of work, workforce intelligence, and organizational effectiveness analytics.
• Detect emerging workforce signals, structural shifts, risks, and opportunities before they appear in business plans.
• Translate model outputs into executive-ready recommendations on workforce investments, organization design, and transformation.
• Operate autonomously as a hands-on senior individual contributor, influencing decisions through expertise and trusted advisory relationships.

Required qualifications

• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Operations Research, or a related quantitative field; master's degree or PhD preferred.
• At least 8 years of hands-on data science, forecasting, simulation, or advanced analytics experience in workforce planning, workforce intelligence, operations, or business strategy.
• Advanced proficiency in Python or R and SQL, including forecasting, simulation, and machine-learning libraries; experience developing and validating end-to-end models.
• Experience with relational databases and cloud platforms such as Databricks, Snowflake, or Azure, including creating analysis-ready datasets from imperfect data.
• Working knowledge of strategic workforce planning, organization design, labor-market data, and AI-enabled workforce transformation.
• Ability to communicate technical findings to senior non-technical leaders and turn evidence into actionable recommendations.

Scope and impact

Compensation

• Base pay range is $122,100–$162,800; the posting does not specify a pay cadence.
• Additional compensation, such as an annual bonus or long-term incentives, may be offered.