Senior/Lead Data Scientist

McKesson-posted 2 days ago

Full-time • Mid Level

Irving, TX

1,001-5,000 employees

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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.

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

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.
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 least two or more relevant domain (pricing, contracting, demand forecasting, supply-chain optimization, commercial analytics, patient/customer experience).
Proven track record working in cross‑functional product/engineering environments.
Supervised/unsupervised learning, time‑series, causal methods/experimentation, optimization
Expert in Python and SQL; proficiency with PySpark; 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 secure development lifecycle for data/ML.
Executive storytelling; ability to translate technical results into decisions and outcomes.

familiarity with LLMs/NLP and retrieval‑augmented workflows preferred.

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