Lead, Workforce Intelligence

Salesforce•Chicago, IL

About The Position

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.

Requirements

Master’s or PhD in a highly quantitative or computational research field (e.g., Computer Science, Data Science, I/O Psychology, Econometrics, or Statistics).
5+ years of experience in data science, people analytics, or applied research, with a track record of delivering scientific insights to leadership (Director+).
Mastery of Python or R and SQL is required.
Candidates must be proficient in performing data manipulation, statistical modeling, and automation within a code-based environment.
Expert-level proficiency in Tableau; experience architecting visuals that simplify complex, heterogeneous enterprise data.
Experience with ML frameworks (e.g., Scikit-learn, PyTorch, TensorFlow) and advanced techniques, including NLP, clustering, or LLM-driven labeling.
Deep understanding of experimental design, causal inference, and computational statistics.
Exceptional skills in translating complex technical concepts for non-technical audiences and informing specific team or stakeholder actions.
Proven ability to operate with moderate autonomy, handling day-to-day projects independently while seeking guidance for high-level strategy and prioritization.

Responsibilities

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.
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.
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.
Lead the strategic narrative on workforce data by translating complex ML outputs into clear, compelling narratives.
Articulate findings clearly to inform specific stakeholder actions, demonstrating how data leads to concrete outcomes.
Remain at the forefront of emerging AI, Agents, and LLMs.
Proactively champion their adoption and train others on new solutions to ensure they integrate into a human-centric talent model.
Partner with cross-functional peers (e.g., Data Engineering, Finance, Business Partners) on projects involving people data or metrics, ensuring alignment and providing high-quality, accurate insights.