Sr. Data Scientist, Pharmaceutical Supply Chain

McKesson•Irving, TX

2d•$136,300 - $227,100•Hybrid

About The Position

The Lead Business Strategic Insights role partners closely with leaders across Commercial Strategy & Innovation (CSI) to develop data-driven solutions that accelerate business growth, enhance customer outcomes, and bring innovative products to market. This individual will leverage advanced analytics, artificial intelligence, machine learning, and business insights to solve complex challenges, uncover opportunities, and drive strategic decision-making across the organization. This role will participate throughout the full solution lifecycle, from ideation and proof of concept through production deployment and business adoption. Success in this position requires a combination of analytical expertise, business acumen, technical proficiency, and stakeholder influence.

Requirements

Bachelor's degree in Business, Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field, or equivalent experience.
Typically requires 7+ years of relevant professional experience in analytics, data science, business intelligence, strategy, or a related discipline.
5+ years of experience in analytics, data science, business intelligence, or a related field.
Proven experience leveraging analytics to drive measurable business value and strategic outcomes.
Strong understanding of software engineering principles and API development used to commercialize analytical products and solutions.
Advanced communication, presentation, and stakeholder management skills.
Hands-on experience with Python, including libraries such as Pandas, Scikit-learn, NumPy, and related tools.
Advanced SQL skills with experience querying and manipulating large, complex datasets.
Strong understanding of machine learning techniques, including clustering, regression, anomaly detection, forecasting, classification, deep learning, and time series analysis.
Experience working with cloud-based data platforms and modern analytics ecosystems.
Experience building, deploying, or leveraging AI and generative AI solutions within business environments.

Nice To Haves

Experience working in healthcare, distribution, pharmaceutical, or supply chain environments.
Experience with experimental design, causal inference, and business impact measurement.
Familiarity with modern AI frameworks, large language models (LLMs), and agentic AI solutions.
Experience building scalable data products and deploying machine learning models into production environments.
Demonstrated success influencing business decisions through analytics and strategic insights.
Passion for using data to solve complex business problems.
Strong business acumen and strategic thinking capabilities.
Ability to balance technical depth with business priorities.
Self-starter with a continuous improvement mindset.
Strong collaboration and relationship-building skills across functions and leadership levels.

Responsibilities

Accelerate AI-enabled decision-making by partnering with data governance, product, and technology teams to leverage governed data assets, semantic layers, and AI/LLM technologies that enable business users to access trusted insights more efficiently.
Design and develop heuristics, optimization models, and advanced analytics solutions across customer, product, and operational domains to improve business outcomes.
Utilize qualitative and quantitative data to identify opportunities, develop hypotheses, pilot proof-of-concept solutions, and scale successful models across the enterprise.
Apply data science and analytical methodologies to identify market trends, process improvement opportunities, strategic synergies, and actionable business recommendations.
Support digital data acquisition and integration strategies while building scalable data pipelines that complement existing enterprise data assets.
Leverage CSI's data infrastructure to develop next-generation analytical models focused on customer value creation, margin expansion, and operational efficiencies.
Drive experimentation and causal learning through pilots, A/B testing, and other measurement methodologies to quantify business impact and improve decision-making.
Break down complex business challenges into scalable workstreams and collaborate across functional teams to drive solution adoption and implementation.
Develop and deploy scalable Python-based APIs and data products that integrate with enterprise applications and business systems.
Build and scale AI agents that enable business users to interact with data using natural language, uncover insights, identify opportunities, and make faster, more informed decisions.
Present findings, recommendations, and business cases to senior leaders and cross-functional stakeholders.