Associate Director, Oncology Clinical Intelligence & Applied Analytics

In this role, you will operate where evidence generation meets hands-on analytics, partnering with stakeholders to pinpoint the highest-value evidence gaps and leading the delivery that closes them. You will move seamlessly from portfolio-level prioritization with Global Product Teams to building rigorous multimodal patient models or external control arm analyses that inform development and access.

Hematology is growing rapidly. As the technical specialist for clinical intelligence and real-world evidence, you will translate complex data into decision-ready insights, build persistent intelligence that compounds across use cases, and help steer how the portfolio advances for patients.

Accountabilities

• Evidence Gap Prioritization: Partner with Hematology stakeholders and Global Product Teams to identify and prioritize evidence needs across the product lifecycle, including Phase 3 investment decisions, subpopulation discovery, and trial design.
• AI and Causal Inference Analytics: Apply machine learning and causal inference to deliver robust answers on patient stratification, external control arm construction, prognostic risk adjustment, and treatment effect heterogeneity, ensuring analyses meet regulatory and HTA expectations.
• Multimodal Patient Models: Deliver and validate patient-level models that integrate clinical, genomic, imaging, and real-world data for deployment in clinical trials or routine care, with emphasis on reproducibility and rigorous validation.
• Decision-Ready Insights: Turn internal and competitor trial data, alongside real-world data, into clear insight on standard of care, patient pathways, benchmarking, and unmet need to sharpen development and access strategies.
• Platform Enablement: Work with platform and tooling teams to bring new tools, agents, and experimental approaches into evidence generation, and build Hematology-specific intelligence that persists within the Phase 3 Investment Decision Intelligence Foundation.
• Cross-Functional Collaboration: Connect strategic evidence needs with technical delivery, aligning outputs to development, regulatory, and access milestones; collaborate with Translational Science and Clinical Development to incorporate novel signals such as digital endpoints, pathology AI, and biomarker panels.

Essential Skills/Experience

• Advanced degree (PhD or equivalent) in a quantitative discipline - epidemiology, biostatistics, computational biology, machine learning, health data science, or a related field.
• 5+ years of experience spanning both evidence strategy and real-world evidence and/or advanced analytics and data science.
• Strong methodological foundation in causal inference and observational study design, including propensity score methods, instrumental variables, target trial emulation, and comparative effectiveness research.
• Hands-on experience with machine learning and multimodal modeling, including supervised and unsupervised methods, deep learning for imaging or molecular data, and integration of heterogeneous data types into patient-level models.
• Experience building or contributing to external control arms, trial simulators, or prognostic models using real-world and/or clinical trial data.
• Understanding of regulatory and health technology assessment (HTA) evidence standards, with the ability to design analyses that meet the evidentiary bar for submissions and payer engagement.
• Proficiency in Python, R, and SQL, and familiarity with cloud-based analytics environments.

Desirable Skills/Experience

• Domain expertise in hematology or oncology and familiarity with disease-specific endpoints, pathways, and standards of care.
• Track record of influencing cross-functional strategy with evidence, engaging product and clinical leaders to drive decisions.
• Experience deploying analytics into clinical trial operations, submissions, or payer engagements.
• Publications, conference presentations, or open-source contributions in causal inference, multimodal modeling, or real-world evidence.
• Hands-on experience with cloud platforms and MLOps practices to scale models and pipelines.
• Experience integrating digital endpoints, pathology AI, or biomarker panels into evidence strategies.
• Ability to design reusable data and model assets that generalize across indications and studies.