Senior/Lead Data Scientist
Description
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 Senior / Lead Data Scientist is responsible for driving 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’s businesses. This role partners with business stakeholders to translate requirements into technical solutions, ensures robust model governance and performance benchmarking, and pioneers innovative analytical approaches that improve operational efficiency and market competitiveness.
The Data Scientist provides deep technical leadership in modern ML methods, including time-series forecasting, optimization, simulation, causal inference, and LLM/NLP where appropriate . 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
- Identify opportunities for leveraging company 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, and optimize resource 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, and NLP/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 , and maintain ML models in production environments. Ensure scalability, reliability, and compliance with enterprise standards
- Build and maintain scalable data infrastructure to support both real-time and batch decisioning. Leverage cloud-native tools and platforms to optimize performance 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 and maintain scalable data systems and infrastructure that empower our business teams to make better decisions
Minimum Job Qualifications (Knowledge, Skills, & Abilities) :
Education/Training –
Bachelors in math, statistics, engineering, or another STEM field or equivalent experience and typically requires 8 + years of relevant experience. Less years required if has relevant Master’s or Doctorate qualifications.
Business Experience –
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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.
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Experience in at least two or more relevant domain (pricing , contracting, demand forecasting, supply-chain optimization, commercial analytics, patient/customer experience).
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Proven track record working in cross‑functional product/engineering environments .
Specialized Knowledge/Skills –
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Supervised/unsupervised learning, time‑series, causal methods/experimentation, optimization; familiarity with LLMs/NLP and retrieval‑augmented workflows preferred.
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Expert in Python and SQL; proficiency with PySpark ; experience with Azure ML, MLflow , model registries, monitoring/telemetry (e.g., Evidently ) and CI/CD.
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Git, testing, packaging, pipelines; containerization; performance/cost tuning in cloud; observability and on‑call patterns for ML services.
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Feature engineering , working knowledge of healthcare/commercial data sets .
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Demonstrated adherence to enterprise cybersecurity standards and secure development lifecycle for data/ML.
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Executive storytelling; ability to translate technical results into decisions and outcomes.
Working Conditions:
Environment (Office, warehouse, etc.) –
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Traditional office environmen t .
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. 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.
McKesson is an Equal Opportunity Employer
McKesson provides equal employment opportunities to applicants and employees and is committed to a diverse and inclusive environment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age or genetic information. For additional information on McKesson’s full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page.