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 Description
Role: Senior Data Scientist – Forecasting ProductOrganization: Enterprise Data Science
The Senior Data Scientist – Forecasting Product focuses on building, improving, and operationalizing a core forecasting product, then supporting its rollout and adoption across multiple enterprise use cases. This role emphasizes applied forecasting, analytical rigor, and collaboration to ensure forecasting solutions are reliable, trusted, and scalable in practice.
The role is hands-on and execution-focused, working closely with product owners, business partners, and downstream teams to ensure the forecasting product delivers consistent value as it expands across the enterprise.
Position Objectives
Build and mature a core forecasting product that supports planning and decision-making
Improve forecast quality, stability, and usability for real-world applications
Support the rollout of the forecasting product across teams, domains, and use cases
Leverage modern techniques, including LLMs where appropriate, to enhance forecast understanding and decision support
Contribute to measurable business impact through forecasting improvements
Key Responsibilities
Forecasting Product Development
Develop, test, and improve forecasting models using statistical, machine learning, and deep learning approaches
Apply forecasting techniques to core product use cases (e.g., demand, volume, usage, financial, or capacity forecasting)
Analyze forecast performance, bias, and stability across segments and time horizons
Develop probabilistic forecasts and uncertainty estimates where appropriate
Ensure forecasting outputs are reliable, interpretable, and suitable for repeated use
Product Maturity & Reusability
Help standardize forecasting assumptions, evaluation metrics, and outputs
Contribute to making forecasting solutions reusable across multiple applications
Document model behavior, limitations, and expected usage patterns
Explore the use of LLMs to improve forecast explainability, summarization, and user understanding
Support continuous improvement of the forecasting product based on user feedback and performance data
Enterprise Rollout & Adoption
Support rollout of the forecasting product to new business domains and teams
Partner with stakeholders to understand how forecasts are consumed in different contexts
Help adapt forecasting outputs to meet varied decision needs while maintaining core consistency
Assist with onboarding users and explaining forecast behavior, uncertainty, and trade‑offs
Identify adoption gaps and opportunities to improve usability and trust
Collaboration with Downstream Teams
Work with planning, optimization, finance, or analytics teams to ensure forecasts are fit for downstream use
Align on forecast expectations such as horizon, refresh cadence, and confidence bounds
Help determine whether limitations in business outcomes are driven by forecast quality or downstream decision logic
Technical Excellence
Follow best practices for experimentation, evaluation, and validation
Write clear, maintainable Python code for data analysis and modeling
Contribute to shared forecasting utilities and reusable components as needed
Document methodologies, assumptions, and key trade‑offs
Stakeholder Communication
Communicate forecasting results, uncertainty, and limitations clearly to non‑technical audiences
Support adoption by helping users understand when and how to rely on forecasts
Collaborate effectively with cross‑functional partners in ambiguous problem spaces
Minimum Requirements
Experience: 4–7+ years of data science, machine learning, or advanced analytics experience, including applied forecasting work
Education: Bachelor’s degree required; Master’s degree preferred in Statistics, Applied Mathematics, Operations Research, Computer Science, or a related quantitative field
Demonstrated experience applying forecasting models to real business or operational problems
Critical Skills & Qualifications
Strong experience with time series forecasting (statistical, ML, and/or deep learning)
Solid understanding of forecast evaluation, bias analysis, and uncertainty
Proficiency in Python for data analysis and modeling
Ability to apply forecasting techniques across multiple business contexts
Strong analytical and problem‑solving skills
Clear communication of technical findings and trade‑offs
Collaborative mindset and comfort supporting product rollout
Nice‑to‑Have Knowledge & Skills
Experience working with Large Language Models (LLMs) for tasks such as summarization, explanation, decision support, or workflow automation
Familiarity with prompt design, evaluation, or integrating LLM outputs into analytics products
Experience supporting enterprise rollout or scaling of analytics products
Familiarity with probabilistic forecasting, scenario analysis, or simulation
Exposure to cloud platforms or MLOps workflows
Experience working with large‑scale enterprise data systems
Strong written communication skills
At McKesson, we care about the well‑being of the patients and communities we serve, and that starts with caring for our people. That’s why we have a Total Rewards package that includes comprehensive benefits to support physical, mental, and financial well‑being. Our Total Rewards offerings serve the different needs of our diverse employee population and ensure they are the healthiest versions of themselves.
As part of Total Rewards, we are proud to offer a competitive compensation package at McKesson. 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 McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long‑term incentive opportunities may be offered.
Our Base Pay Range for this position
€69,800 - €116,300
McKesson job postings are posted on our career site: careers.mckesson.com.
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