Détails du poste

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 Product
Organization: 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 supportphysical, 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

McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind:

McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.

McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.

McKesson job postings are posted on our career site: careers.mckesson.com.