Lead - Workforce Intelligence

Salesforce Salesforce · Enterprise · Seattle, Illinois - Chicago, WA

Salesforce is seeking a Lead, Workforce Intelligence to shape the future of employee listening and organizational research. This role combines data science, data engineering, behavioral science, and people analytics to transform workforce data into scalable intelligence for strategic decisions. The role involves designing analytical frameworks, developing ML and LLM solutions, creating reusable intelligence assets, and operationalizing capabilities. Responsibilities include analyzing employee feedback, collaboration patterns, and communication signals using NLP, network analysis, ML, and LLMs. The role also involves partnering with data engineering and technology teams to operationalize workforce sensing capabilities and leading the development of organizational network analysis. The candidate should have strong technical depth, intellectual curiosity, and the ability to influence stakeholders.

What you'd actually do

  1. Design and execute innovative workforce intelligence studies that combine employee listening, behavioral science, and advanced analytics to better understand employee experiences and organizational outcomes.
  2. Identify, evaluate, and operationalize emerging workforce data sources and employee listening data.
  3. Develop scalable approaches for transforming unstructured workforce data into reusable intelligence assets through methods such as topic modeling, embeddings, large language models, clustering, and classification.
  4. Build analytical frameworks and data products that enable workforce insights to be generated consistently, efficiently, and at scale.
  5. Partner closely with Data Engineering, HR Technology, and platform teams to define technical requirements, data models, and architectures that support workforce intelligence initiatives.

Skills

Required

  • Data science
  • Data engineering
  • Behavioral science
  • People analytics
  • Machine learning
  • LLM solutions
  • Natural language processing
  • Network analysis
  • Large language models
  • Topic modeling
  • Embeddings
  • Clustering
  • Classification
  • Organizational network analysis
  • Collaboration intelligence
  • Responsible AI
  • Data governance
  • Research methodology

Nice to have

  • Computational social science
  • Employee research
  • Applied AI
  • Employee listening
  • Workforce sensing
  • Consulting mindset

What the JD emphasized

  • transform complex workforce and employee experience data into scalable intelligence
  • transform workforce data into actionable insights
  • scalable approaches for analyzing employee feedback, collaboration patterns, communication signals, and emerging workforce datasets
  • transform fragmented workforce data into scalable intelligence
  • identify and operationalize new sources of workforce insight
  • develop innovative approaches for analyzing employee experience and collaboration data
  • create reusable intelligence assets
  • operationalize new workforce sensing capabilities
  • develop scalable approaches for transforming unstructured workforce data into reusable intelligence assets
  • Build analytical frameworks and data products that enable workforce insights to be generated consistently, efficiently, and at scale
  • operationalize new analytical capabilities
  • Lead the development of organizational network analysis and collaboration intelligence capabilities
  • Design scalable approaches for coding, categorizing, summarizing, and synthesizing large volumes of employee feedback and unstructured workforce data
  • Stay current on advances in AI, computational social science, workforce sensing, employee listening, and organizational research methodologies

Other signals

  • AI CRM
  • agentic era
  • workforce intelligence
  • employee listening
  • organizational research
  • data science
  • data engineering
  • behavioral science
  • people analytics
  • machine learning
  • LLM solutions
  • workforce sensing
  • natural language processing
  • network analysis
  • large language models
  • responsible AI