Senior 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.

Current Need
The Senior Data Scientist (DS) will lead the development of advanced analytics and AI solutions that enhance decision intelligence, operational efficiency, and risk management across Oncology & Multispecialty (O&M) Finance. The Sr DS is the core model builder and analytical executor, turning prioritized use cases into working models and insights. This role balances deep technical work with business context, and is critical to delivering fast, credible results.

Key Responsibilities
Advanced Analytics and AI Execution

Help define and subsequently execute the approach for 1-2 O&M Finance use cases, e.g., Fraud & Risk Management, Financial Controller / Agentic AI, Forecasting, Collections Improvement Analytics

Develop time series forecasting models (short‑term, long‑term, sparse data), anomaly detection models (transactions, journals, contracts, pricing), scenario modeling for finance decisions, document entity extraction, and agentic workflows

Explore data products and other upstream sources, evaluate data readiness and quality issues, and engineer features from transactional, contract, and time‑series data

Apply statistical rigor to validate accuracy improvements vs legacy methods

Partner with ML engineering support to harden models for production, and help implement monitoring, drift detection, and retraining strategies

Partner with the Technical Product Manager and Lead DS to define scope, success metrics, and solution iteration plans

Ensure explainability and auditability for finance and SOX contexts where necessary

Stakeholder Engagement

Translate business requirements into technical models and logic

Work directly with Finance SMEs to validate assumptions, interpret outputs, and refine models based on real world constraints

Builds strong, trust‑based relationships with stakeholders, including business units, data engineering, and executive leadership through high quality work

Understand and resolve issues that arise during UAT in a well‑organized manner

Communication and Documentation

Communicate progress, risks, and dependencies to product leadership and seek leadership support where necessary

Create and maintain product documentation, including technical specs, release notes, and user guides

Minimum Requirement
Degree or equivalent and typically requires 7+ years of relevant experience

Critical Skills

7+ years of professional experience in progressively advancing data science and applied AI/ML roles

Strong background in forecasting, classification, anomaly detection / risk modeling, statistical modeling

Experience working with financial data (AR/AP, GL, contracts)

Clearly demonstrated experience working with Python, SQL

Solid understanding of modern digital architectures, APIs, cloud platforms, data ecosystems, and software development practices

Comfortable explaining model outputs to non‑technical audiences

Additional Skills

Demonstrated experience implementing Agent workflows and/or LLM‑augmented analytics

Experience supporting SOX, audit, or financial controls

Prior work in healthcare, life sciences, or complex B2B finance

Experience with Power BI or similar BI tools

Exposure to ERP / finance systems (SAP, People Soft, etc.)

Working Conditions

In office requirement, we are Flex and Connect with 2 days a week in office

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…