Head, Center of Oncology Data Excellence (CODE)

Role Summary

Head, Center of Oncology Data Excellence (CODE) leading cross-brand, cross-tumor data strategy and advanced analytics within Global Medical Affairs Oncology; embedding GenAI methodologies to deliver trusted, reproducible, globally scaled evidence for patients and the business.

Responsibilities

• Lead CODE Strategy: Define and implement a unified approach across Data Analytics, Global Data Strategy, Biostatistics, and Scientific Medical Writing.
• Work with stakeholders in EG2P and GMA to ensure research is timely and scientifically rigorous.
• Data Innovation: Oversee in-house RWE studies and analyses with high scientific standards.
• Scale Analytics Globally: Deliver coordinated capabilities to reduce cycle times and improve reproducibility across brands and regions.
• Data Strategy: Oversee global data strategy and data asset decisions in partnership with stakeholders across EG2P and AZ.
• Delivery excellence: Ensure robust design, analysis, and interpretation for GMA-led trials, externally sponsored research, and RWE/RWD studies with governance.
• Scientific Medical Writing: Lead development and QC of SDCs, protocols, study reports, abstracts, and presentations; leverage GenAI when appropriate.
• Accelerate GenAI Adoption: Promote responsible GenAI with guardrails, metrics, and human-in-the-loop validation; enhance analytics and writing with GenAI capabilities.
• Drive End-to-End Integration: Align cross-functional teams from study build to evidence dissemination.
• Methods and tooling: Advance modern analytics (causal inference, target trial emulation, etc.), statistical computing practices, and GenAI copilots.
• Standardise Excellence: Develop shared playbooks and reusable assets to accelerate delivery and consistency.
• Governance & Compliance: Co-lead data and AI governance forums to meet regulatory expectations and inspection readiness.
• Infrastructure Leadership: Oversee scalable data environments and pipelines.
• Capability Building: Champion continuous learning in GenAI, advanced statistics, and data engineering.
• Programme Delivery: Own CODE milestones, delivery plans, and risk management across global programmes.
• Impact Measurement: Quantify business and patient impact through insights and publications.

Qualifications

• PhD, MD, PharmD, or MS in outcomes research, epidemiology, biostatistics, statistics, data science, or related field.
• 10+ years in pharma/biotech or health data, leading advanced analytics and evidence programmes; recognized authority in oncology and real-world research.
• Leadership: team leadership, matrix management, stakeholder influence, cross-functional program delivery; vendor oversight and multisector collaboration.
• Communication: outstanding written and verbal communication; ability to translate complex methods into actionable strategies and outputs.
• Strong in RWE/RWD methods, study design, biostatistics, and R/Python; familiar with modern analytics engineering.
• Deep understanding of data integration, privacy standards (HIPAA, GDPR), and operationalizing compliant AI/ML/GenAI.
• Validated use of GenAI to improve analytics and scientific writing with measurable impact.

Skills

• GenAI governance and adoption
• RWE/RWD study design and analysis
• Statistical computing (R, Python)
• Data strategy and asset management
• Biostatistics and epidemiology
• Scientific medical writing and publications
• Cross-functional program leadership
• Regulatory compliance and data privacy

Education

• PhD, MD, PharmD, or MS in relevant field as listed above

Additional Requirements

• In-person minimum three days per week with flexibility