The Lead Workforce Scientist develops advanced models, methodologies, and analytical assets that connect employee voice with workforce, operational, and business outcomes. These capabilities are deliberately built to discover patterns beyond the questions the organization is already asking, expanding what Workforce Intelligence is capable of knowing. The role applies statistical modeling, survey science, natural language processing, and responsible AI to convert structured and unstructured listening signals into enterprise insight and practical recommendations. The ideal candidate is equally energized by the workforce question and the analytical challenge, and can translate complex evidence into clear guidance for senior leaders while protecting employee privacy and trust.
KEY RESPONSIBILITIES
Data Science & Modeling | Analytical Data Development & Reproducibility
Build and validate statistical and machine learning models, including but not limited to regression, classification, survival analysis, clustering, and key-driver analysis, to explain and predict engagement, retention, culture, and organizational health outcomes.
Design rigorous analyses, including quasi-experimental and causal inference approaches, to assess the impact of programs, leader actions, and listening interventions.
Apply survey science and psychometrics, including sampling, weighting, reliability, validity, and measurement invariance, to maintain the credibility of listening data.
Conduct open-ended discovery where the question is not fully defined: test hypotheses, explore anomalies and weak signals, identify previously unseen relationships, and communicate the confidence and limitations of findings.
Use Python or R and SQL to integrate, transform, and analyze survey, lifecycle, HRIS, operational, and business data at enterprise scale.
Develop fit-for-purpose analytical datasets and derived measures that connect employee sentiment and experience signals with workforce and business outcomes.
Work in cloud data platforms such as Databricks, using version control, documentation, and reproducible methods so analyses and models can be reviewed, reused, and maintained.
Partner with HR data platform and technology teams to define data requirements, quality expectations, and sustainable access to listening data.
AI, NLP & Innovation | Domain Leadership & Advisory
Develop and evaluate natural language processing approaches, including topic modeling, embeddings, sentiment classification, and LLM-assisted thematic analysis, to analyze open-ended feedback at scale.
Assess AI-enabled listening tools and vendor models for accuracy, bias, explainability, and fitness for intended use before they inform decisions.
Combine structured workforce data with unstructured experience signals while maintaining privacy, confidentiality thresholds, transparency, and appropriate governance.
Apply responsible AI principles so insights are explainable, ethical, and designed to support action rather than surveillance.
Serve as an enterprise subject-matter leader for employee listening, engagement, culture, and employee experience analytics, helping shape the research and modeling agenda.
Peer around corners by detecting signals, unexpected relationships, and emerging employee experience risks or opportunities before leaders know to ask about them; frame the next questions the organization should investigate.
Translate complex findings into executive-ready narratives, implications, and recommendations for HR leaders, COEs, HRBPs, and business leaders.
Operate with significant autonomy as a hands-on senior individual contributor, shaping decisions through expertise, influence, and trusted advisory relationships.
REQUIRED QUALIFICATIONS
Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Industrial-Organizational Psychology with a quantitative focus, or a related field; master's degree or PhD preferred.
8+ years of hands-on experience applying data science, advanced analytics, or quantitative research to employee, people, or customer experience questions.
Advanced proficiency in Python or R and SQL, including common data science libraries, with demonstrated experience developing and validating models end to end.
Applied experience with natural language processing and text analytics on open-ended feedback, including topic modeling, embeddings, sentiment classification, or LLM-assisted analysis.
Experience working with relational databases and cloud data platforms such as Databricks, Snowflake, or Azure, including creating analysis-ready datasets from imperfect source data.
Ability to communicate technical findings to senior, non-technical leaders and translate evidence into practical business recommendations.
SCOPE & IMPACT
Leads enterprise-wide listening analytics and modeling initiatives with significant strategic impact.
Defines new analytical questions, methodologies, and enterprise points of view that reveal risks and opportunities beyond known business requests.
Influences VP, SVP, HRLT, COE, HRBP, and business leadership decisions through evidence and trusted advisory.
Creates reusable analytical methods and models that expand what Workforce Intelligence and HR are capable of discovering and knowing.
We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.
Our Base Pay Range for this position
$122,100 - $162,800
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