Director, Decision Science for Clinical Operations

Role Summary

Director, Decision Science for Clinical Operations. Lead a high-performing decision science team to embed analytics into Global Clinical Operations, partnering with senior R&D leadership to shape the future of clinical trial planning and execution. Requires on-site presence 2-3 days per week.

Responsibilities

• Strategic & Facilitative Leadership: Partner with GCO‚Äôs Decision Support to frame complex problems, develop options, and define analytical pathways; synthesize evidence to guide strategy; translate model outputs into actionable recommendations for executives.
• Advanced Modeling & Analytics: Implement decision models to quantify risk and opportunities in timelines and resources; deliver predictive models for recruitment, site performance, and milestones; identify value-add opportunities through innovative analytics.
• Technical Innovation & Capability Development: Design and deploy scalable analytics solutions, production-ready models, monitoring systems, and data pipelines; promote DevOps practices; train colleagues on tools and decision frameworks.

Qualifications

• Basic Qualifications:
  • PhD (preferred) or MSc in a quantitative field with extensive pharmaceutical/biotech experience
  • Proven expertise designing flexible decision models (Monte Carlo, Bayesian, scenario/sensitivity analysis)
  • Python or R production-level coding experience
  • DevOps experience (Git, CI/CD, testing)
  • Experience leading technical teams and delivering data science solutions with business impact
  • Strong communication to distill analyses into recommendations for senior leadership
• Preferred Qualifications:
  • Deep knowledge of drug development and Clinical Operations workflows
  • Therapeutic area experience and regulatory/trial design knowledge
  • Direct Clinical Operations experience
  • Management consulting in pharmaceutical strategy/operations
  • Expertise in Bayesian statistics, machine learning, enrollment simulation

Skills

• Statistical modeling and data science
• Operational analytics for clinical trials
• Programming in Python or R; production-grade coding
• DevOps practices and CI/CD
• Communication and influence with senior leadership

Education

• PhD (preferred) or MSc in Data Science, Statistics, Computer Science, Operations Research, or Decision Analysis