Lead Workforce Scientist

Role overview

Modeling and analytics

• Build demand and supply forecasts using time-series, regression, and machine-learning methods to connect headcount, skills, capacity, and cost to business drivers.
• Develop Monte Carlo, Markov, and stock-and-flow scenarios and simulations to test uncertainty and quantify future workforce needs.
• Validate predictive models for talent flows, capacity, capability gaps, and workforce outcomes, assessing accuracy, stability, and fairness.
• Apply statistical and causal methods to spans and layers, operating models, productivity, and capacity.
• Use Python or R and SQL with enterprise HRIS, finance, operational, skills, and labor-market data; create fit-for-purpose analytical datasets and derived measures.
• Use cloud platforms such as Databricks, version control, documentation, and reproducible methods; partner with data, finance, and technology teams on data quality and access.

Future of work and advisory

• Model task-level exposure and workforce implications of AI, automation, and emerging technologies across roles, skills, capacity, and cost.
• Develop scenarios that distinguish human, automated, and AI-augmented work and make assumptions and uncertainty clear.
• Evaluate labor-market evidence and signals to inform future skills and talent availability.
• Partner with technology and product teams on AI-enabled forecasting and decision support using responsible AI standards.
• Serve as an enterprise subject-matter leader, detect emerging workforce risks and opportunities, and shape the modeling agenda.
• Translate model outputs into executive-ready recommendations on workforce investment, organization design, and transformation; operate autonomously as a hands-on senior individual contributor.

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.
• 8+ years of hands-on data science, forecasting, simulation, or advanced analytics experience applied to workforce planning, workforce intelligence, operations, or business strategy.
• Advanced proficiency in Python or R and SQL, including forecasting, simulation, and machine-learning libraries, and end-to-end model development and validation.
• Experience with relational databases and cloud data platforms such as Databricks, Snowflake, or Azure, including creating analysis-ready datasets from imperfect source 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

Base pay

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