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What you do at McKesson matters. We foster a culture where you can grow make an impact and are empowered to bring new 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 tomorrows 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 prospect 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 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

Bachelors degree in Data Science Statistics Computer Science Mathematics Economics Industrial-Organizational Psychology with a quantitative focus or a related field; masters 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 McKessons 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 pleaseclick here.

Our Base Pay Range for this position

$122100 - $162800

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McKesson job postings are posted on our career site: .

McKesson is an Equal Opportunity Employer

McKesson provides equal employment opportunities to applicants and employees without regard to race color religion sex sexual orientation gender identity national origin protected veteran status disability age genetic information or any other legally protected category. For additional information on McKessons full Equal Employment Opportunity policies visit our Equal Employment Opportunity page.

McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted.

Join us at McKesson!

Required Experience:

IC

📌 Lead Workforce Scientist (Mississauga)
🏢 McKesson
📍 Mississauga