Senior/Lead Data Scientist

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.

Senior Lead Data Scientist 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.

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.

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

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

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.

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;

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.

Working

Conditions

Traditional office

environment.

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, equ