Lead, Workforce Intelligence
Salesforce - Illinois - Chicago; Washington - Seattle; Texas - Austin; Georgia - Atlanta; Washington - Bellevue; Indiana - Indianapolis
Posted May 14, 2026
Benefits
- Parental leave
- Not verified not verified - source not recorded
- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Source-linked last checked May 7, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked May 7, 2026.
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Lead, Workforce Intelligence Illinois - Chicago; Washington - Seattle; Texas - Austin; Georgia - Atlanta; Washington - Bellevue; Indiana - Indianapolis Lead, Workforce Intelligence Salesforce is seeking a Lead in Workforce Intelligence with a specialized focus on Machine Learning and Applied Research. In this role, you will drive the end-to-end lifecycle of workforce research - from problem formulation and experimental design to the delivery of high-fidelity predictive models. You will combine deep technical expertise in Machine Learning with a consulting mindset to help leaders decode complex workforce patterns and employee experiences. This is an individual contributor (IC) role; you will not have direct reports, but will be expected to lead through influence, technical expertise, and cross-functional collaboration. You will be expected to drive impact through computational rigor and technical evangelism, transitioning ad-hoc research into scalable, reproducible, and automated tools that provide real-time guidance to the business. Key Responsibilities Strategic ML & Problem Formulation : Define ambiguous business challenges as rigorous research questions. Assess and prioritize new work for scope and urgency, managing stakeholder expectations and pivoting based on changing business needs. Predictive & Causal Modeling: Conduct mid-to-high complexity data analyses, building robust Machine Learning models (predictive and descriptive) on structured and unstructured enterprise data. Lead the development of causal identification strategies to determine the effectiveness of talent initiatives. Data Productization & Visualization: Drive the "productization" of data by architecting high-impact, self-service analytics tools. Ensure models and dashboards integrate seamlessly with existing business tools to provide actionable, real-time insights. Evidence-Based Storytelling
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