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 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 tomorrow’s health today, we want to hear from you.
Job Title:
Senior / Lead Data Scientist
Current Need:
The ideal candidate will reside in either Irving, TX or Toronto, Canada.
The Senior / Lead Data Scientist is responsible fordriving the full lifecycle of advanced analytics and machine learning solutions—from problem framing and hypothesis design to production deployment and continuous monitoring—delivering measurable business outcomes for McKesson’sbusinesses. This role partners with business stakeholders to translate requirements into technical solutions, ensures robust model governance and performance benchmarking, andpioneersinnovative analytical approaches that improve operational efficiency and market competitiveness.
The Data Scientist provides deep technical leadership in contemporary ML methods, including time-series forecasting, optimization, simulation, causal inference, and LLM/NLP whereappropriate. In addition, the role works closely with product, engineering, and business teams, champions McKesson’s enterprise model development standards, and upholds the company’s ILEAD leadership principles.
Key Responsibilities




Identifyopportunities forleveragingcompany data to drive innovative and scalable machine learning solutions that address complex business challenges. Develop and implement strategies that enhance operational efficiency, automate decision-making, improve customer outcomes, andoptimizeresource allocation. Apply advanced analytics to evaluate organizational performance, simulate potential impacts of strategic changes, and support initiatives across domains such as predictive modeling,forecasting,classification, recommendation systems, anomaly detection, andNLP/LLM.
Develop custom machine learning models and algorithms tailored to business needs. Apply these models to large datasets to generate actionable insights and support strategic decision-making
Collaborate with cross-functional teams to deploy,monitor, andmaintainML models in production environments. Ensure scalability, reliability, and compliance with enterprise standards
Build andmaintainscalable data infrastructure to support both real-time and batch decisioning. Leverage cloud-native tools and platforms tooptimizeperformance and cost
Engage with business stakeholders to translate requirements into technical solutions. Provide thought leadership and guidance on analytical approaches and data strategy
Ensure model governance, documentation, and performance benchmarking. Maintain compliance with Responsible AI and data privacy standards




Build andmaintainscalable data systems andinfrastructure that empower our business teams to make betterdecisions
Minimum Job Qualifications(Knowledge, Skills, & Abilities):
Education/Training –
Bachelors in math, statistics, engineering, oranother STEM field or equivalent experienceand typically requires8+ years of relevantexperience. Less yearsrequiredifhasrelevantMaster’s or Doctorate qualifications.
Business Experience –
7+ years of hands-on data science experience delivering models to production with measurable business impact;
4+ years leading projects or small teams as a tech lead.
Experience in at leasttwo or morerelevantdomain(pricing,contracting, demand forecasting, supply-chain optimization, commercial analytics, patient/customer experience).
Proventrack recordworking in cross‑functional product/engineering environments.
Specialized Knowledge/Skills –
Supervised/unsupervised learning, time‑series, causal methods/experimentation, optimization;
familiarity with LLMs/NLP and retrieval‑augmented workflows preferred.
Expert in Python and SQL;
ProficiencywithPySpark;
experience with Azure ML,MLflow, model registries, monitoring/telemetry (e.G., Evidently)and CI/CD.
Git, testing, packaging, pipelines;
containerization;
performance/cost tuning in cloud;
observability and on‑call patterns for ML services.
Feature engineering,working knowledge of healthcare/commercial data sets.
Demonstrated adherence to enterprise cybersecurity standards and securedevelopmentlifecycle for data/ML.
Executive storytelling;
ability to translate technical results into decisions and outcomes.
Working Conditions:
Environment (Office, warehouse, etc.) –
Traditional officeenviro

📌 Senior/Lead Applied Data Scientist (Canada)
🏢 McKesson (Sourcer On Req)
📍 Canada