Head, Center of Oncology Data Excellence (CODE)

Role Summary

Head, Center of Oncology Data Excellence (CODE). Lead cross-brand, cross-tumor data strategy and advanced analytics within Global Medical Affairs Oncology, with a focus on 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.
• Collaborate 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 that reduce cycle times and improve reproducibility across brands and regions/therapeutic areas.
• Data Strategy: Oversee global data strategy and make data asset decisions in partnership with stakeholders across EG2P and AZ.
• Delivery Excellence: Ensure robust design, analysis, and interpretation for GMA-led trials, ESRs, and RWE/RWD studies with governance.
• Scientific Medical Writing: Lead development and QC of SDCs, protocols, study reports, abstracts, and presentations; leverage GenAI tools where appropriate.
• Accelerate GenAI Adoption: Champion responsible GenAI deployment with guardrails, metrics, and human-in-the-loop validation; improve analytics and writing with GenAI.
• 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, synthetic controls, time-to-event modeling), statistical computing practices (R/Python, package governance), and GenAI copilots to improve reproducibility and throughput.
• 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 development of scalable data environments and pipelines.
• Capability Building: Promote 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 delivered and publications enabled.

Impact Expectations

• Elevate CODE as the enterprise center of excellence for oncology analytics, biostatistics, data strategy, and scientific medical writing.
• Deliver an integrated data and analytics strategy across brands and regions to reduce cycle times, improve reproducibility, and increase actionable insights.
• Operationalize GenAI across CODE with measurable productivity and quality gains, governance, and human-in-the-loop validation.
• Enable faster, higher quality evidence to inform publications and global medical strategies.

Required Qualifications

• PhD, MD, PharmD, or MS in outcomes research/epidemiology, biostatistics, epidemiology outcomes research, statistics, data science, or related field.
• 10+ years in pharma/biotech or health data, leading sophisticated analytics and evidence programmes; recognized authority in oncology and real-world research.
• Leadership: advanced skills in team leadership, matrix management, stakeholder influence, and cross-functional program delivery; excellence in vendor oversight and multisector collaboration.
• Communication: outstanding written and verbal communication; ability to translate complex methods into actionable strategies and compelling scientific 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 and AI governance
• RWE/RWD study design and analysis
• Biostatistics and data science
• Data integration and privacy compliance
• Strategic leadership and cross-functional collaboration

Education

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