McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring recent ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.

Lead Workforce Scientist (P5) | Focus Area: Employee Listening, Culture & Engagement

Senior Individual Contributor | Workforce Intelligence Data Science, Research & Advisory

POSITION SUMMARY

We are seeking an applied workforce scientist who combines advanced quantitative methods, employee listening analytics expertise, and business judgment to reveal how employees experience the organization and what most influences engagement, culture, retention, and organizational health. The role is designed for complex workforce phenomena and strategic uncertainty, not only defined analytical needs. The Lead Workforce Scientist peers around corners, surfacing the risk, opportunity, or question that leaders did not yet know existed and making previously unseen workforce dynamics visible and actionable.

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 quest

📌 Lead Workforce Scientist (Canada)
🏢 McKesson
📍 Canada