Senior Business Data Analyst

McKesson•Alpharetta, GA

2d•$105,000 - $175,000

About The Position

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. Role Description: AI‑Enabled Decision Intelligence Partner This role collaborates closely with AI systems, analytics platforms, and business stakeholders to deliver executive‑ready insights, decision narratives, and scenario‑based recommendations that directly inform commercial, financial, and operational actions. Acting as the human decision layer between AI agents and leadership, the role ensures that insights are contextually accurate, financially sound, and aligned to business priorities. The individual translates ambiguous business questions into structured analytical frameworks that AI agents and dashboards can execute against, then validates, interprets, and synthesizes those outputs into clear recommendations leadership can act on. The role is accountable not just for analysis, but for decision clarity, narrative quality, and business relevance.

Requirements

Degree or equivalent and typically requires 7+ years of relevant experience
Strong working knowledge of SQL, BI tools, and analytics platforms, with the ability to interrogate data rather than accept outputs at face value.
Experience partnering with AI‑driven analytics, automation, or agent‑based systems to accelerate insight generation.
Deep business understanding with preference for experience in finance, pricing, supply chain, or pharmaceutical distribution environments.
Ability to reason across commercial, financial, and operational dimensions simultaneously.
Exceptional data storytelling skills, with the ability to translate complex analyses into concise, executive‑level narratives.
Comfort presenting insights to senior leaders, walking through assumptions, trade‑offs, and recommendations with confidence and clarity.
Strong judgment in ambiguous situations, with a bias toward actionable recommendations rather than open‑ended analysis.
Ability to balance speed, accuracy, and rigor when supporting time‑sensitive leadership decisions.

Responsibilities

Partner with AI agents, analytics platforms, and dashboards to generate insights related to performance trends, variances, risks, and opportunities.
Validate AI‑generated outputs for business logic, data integrity, financial accuracy, and real-world applicability before executive consumption.
Apply business judgment to contextualize results, identify signal vs. noise, and surface implications that AI alone cannot infer.
Translate high‑level business questions (e.g., revenue leakage, margin erosion, pricing sensitivity, supply disruption) into well-defined analytical frameworks that AI agents and BI tools can execute.
Define assumptions, constraints, scenarios, and comparison baselines to ensure analyses are decision-grade rather than exploratory.
Partner with stakeholders across Finance, Commercial, and Operations to align on definitions, metrics, and interpretation standards.
Develop clear, executive-ready narratives that explain what happened, why it happened, what could happen next, and what action is recommended .
Prepare financial walks, variance explanations, leakage stories, and QBR‑ready insights tailored to senior leadership audiences.
Frame insights in terms of value impact (revenue, margin, cost, working capital, productivity) rather than technical metrics.
Conduct scenario modeling to assess the impact of pricing changes, cost shifts, reimbursement variability, mix changes, and volume assumptions.
Evaluate trade‑offs and sensitivities across multiple scenarios to support leadership decision‑making under uncertainty.
Clearly articulate scenario assumptions, risks, and confidence levels to avoid over-reliance on point estimates.
Act as the quality gate for AI‑assisted insights, ensuring outputs meet enterprise standards for accuracy, defensibility, and compliance.
Maintain consistency in definitions, assumptions, and storytelling across recurring leadership forums (QBRs, MBRs, operating reviews).
Build trust with stakeholders by ensuring insights are transparent, explainable, and aligned with business reality.