Senior Analyst, Planning Intelligence & Enablement

AstraZeneca•Gaithersburg, MD

About The Position

This is a hands-on, high-impact position within an intricate, fast-paced cell therapy supply chain. You will partner with planning, procurement, manufacturing, finance, quality, master data, and digital teams to ensure trusted planning outputs today—and to design the data structures, decision logic, and guardrails that enable AI-orchestrated, human-governed planning in the near future. Accountabilities include SAP Planning Governance, Planning Signal Health Monitoring, Data Integrity and Issue Resolution, Metrics and Audit Routines, Decision-Ready Analysis, Decision-Centric Process Mapping, Data Governance and AI Readiness, Expert Planning Tools Enablement, Exception Management Frameworks, AI-Assisted Workflow Pilots, and converting institutional planning know-how into organized, portable formats.

Requirements

Strong proficiency in SAP and understanding of supply chain transactions, core data, and planning-relevant data structures
Proficient knowledge in Microsoft Excel, including data analysis, reconciliation, and visualization
Experience analyzing data integrity, transaction accuracy, and process compliance within supply chain systems
Good understanding of end-to-end supply chain processes, including material planning, purchasing, inventory management, and production execution
Ability to perform root cause analysis and translate findings into practical business actions
Good interpersonal and teamwork skills to work across planning, procurement, manufacturing, finance, and master data teams
Excellent oral and written communication skills, with the skill to communicate complex findings clearly to collaborators
Understanding of business implications of system and process issues on supply continuity, inventory, and operational performance
Knowledge of cGMP manufacturing and regulatory/compliance requirements for cell therapy and the pharmaceutical industry
6+ years with BS/BA in related field, or 4+ years with MS/MA or MBA in related field

Nice To Haves

Experience with SAP transaction auditing, controls, or data governance
Highly knowledgeable with Power BI, Tableau, Power Query, Power Pivot, or similar analytics tools
Expert experience with planning systems such as OMP
ERP implementation, enhancement, or scaling experience
Experience in pharmaceutical, biotech, or operations related to cell therapy product logistics
Knowledge of Python or VBA for automation and analysis
Professional certifications such as CPIM or ASCM/APICS
Curiosity and interest in understanding how AI/ML models consume and interpret planning data
Eagerness to learn and collaborate with AI tools, digital agents, and automation platforms
Ability to articulate planning logic and decision rules in structured, transferable formats
Comfort with ambiguity and evolving role scope as AI capabilities mature

Responsibilities

Ensure SAP data, planning parameters, and transaction execution consistently produce reliable material requirements and supply signals.
Track and strengthen signal quality across demand, supply, inventory, and procurement so planning outputs remain stable, accurate, and actionable.
Identify, investigate, and resolve data and transaction issues that distort material planning or downstream execution.
Build dashboards, KPIs, and audit checks to measure planning signal strength, data quality, transaction compliance, and overall supply chain health.
Deliver clear, concise insights for S&OE, S&OP, and Tier forums, surfacing risks, weak signals, data issues, trends, and improvement opportunities.
Document decisions, inputs, and logic embedded in current planning workflows to create the blueprint for future AI-enabled planning.
Support master and transactional data governance for operational execution and planning performance; define standards for data quality, structure, completeness, and context that enable high-confidence, autonomous planning over time.
Serve as an authority translating planning logic, SAP transactions, and master data settings into decision rules that future AI agents must replicate or improve.
Establish criteria for when AI-generated plans require human review, issue, or override, with special focus on GxP-critical materials and patient-specific supply chains.
Test AI recommendations against current planning outputs, measure accuracy, identify gaps, and debrief to improve AI agent performance.
Convert institutional planning know-how into organized, portable formats. These include decision trees, logic maps, and exception catalogues. They can be embedded into AI systems and safeguarded as automation scales.