Director, Oncology Clinical Intelligence & RWE Strategy

AstraZeneca; Kendall Square (MA); On-site; USD 180,000 - 230,000; Full time

Job summary

AstraZeneca in Cambridge, MA is building an enterprise AI engine to turn multimodal data into decisive actions across oncology from R&D to patient care. You will drive end-to-end evidence strategy, shape portfolio decisions, and scale AI-enabled insights across trials, regulatory submissions, and market access.

We seek a leader who translates complex analyses into strategic guidance, thrives in a matrixed environment, and partners with Translational Science, Clinical Development, and Data teams

Qualifications

• Translate complex analytical outputs into strategic recommendations.
• Lead without authority across matrixed teams.
• Distill technical complexity for diverse audiences.

Responsibilities

• Own the evidence strategy from R&D through patient care.
• Partner with Translational Science and Clinical Development to integrate data.
• Coordinate with Global Product Teams and external data collaborators.
• Scale reusable AI-enabled evidence across hematology portfolio.

Skills

Leadership

Cross-functional collaboration

Influencing without authority

Job description

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors : you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.

Introduction to role

Are you ready to turn complex, multimodal data into decisive actions that shape the future of oncology ? This role leads a connected evidence strategy spanning R&D through patient care, where your decisions influence trials, filings, and access strategies that reach patients faster. You will help power an enterprise-scale AI and data engine by being an exceptional connector leveraging existing capabilities, scaling what works, and exporting breakthrough ideas across teams and geographies.

Can you see yourself guiding senior stakeholders through complex trade-offs to deliver impact at scale? You will own the end- to- end evidence agenda for an on c ology therapeutic area, aligning it to the highest-priority scientific, medical, and business needs across the lifecycle. By harnessing the PH3ID Intelligence Foundation and rich multimodal assets, you will translate signals into intelligence that advances portfolio investment decisions, strengthens regulatory packages, and sharpens commercial positioning.

You will work at the interface of Translational Science, Clinical Development, and Market Access leading without direct authority in a matrixed environment. Join a team dedicated to building AI-enabled evidence that persists and extends across use cases, helping a fast-growing therapeutic area set the pace for the portfolio.

Accountabilities

• End-to-End Evidence Strategy : Own the clinical intelligence and real-world evidence strategy from R&D through patient care, building a clear roadmap tied to the most critical scientific, medical, and business priorities across development, regulatory, and market access.
• Evidence Gap Prioritization: Define and rank the evidence gaps that matter most, informing PH3ID, trial design, external control arm strategies, and subpopulation discovery to accelerate confident decisions.
• Translational and Clinical Partnership: Partner with Translational Science and Clinical Development to integrate genomics, imaging, digital endpoints, and pathology AI into strategies for patient stratification and biomarker-driven development.
• AI-Enabled Hematology Methods : Advance AI-driven approaches including tumor dynamics forecasting, prognostic risk adjustment, trial simulation, and transportability analyses to de-risk and optimize clinical programs.
• Integrated Insight Generation: Convert internal and competitor trial data, together with real-world data, into actionable insight on standard of care, patient journeys, unmet need, and competitive positioning that guides development and access strategies.
• Stakeholder Influence and Alignment: Influence Global Product Teams, matrix partners, and external data collaborators to ensure evidence outputs are both methodologically rigorous and strategically relevant.
• Compounding Intelligence Foundation : Build an oncology therapeutic area specific intelligence that persists and grows within the PH3ID Intelligence Foundation so evidence compounds across use cases rather than being rebuilt each time.
• Immediate Broader Impact: Deliver early wins that inform ongoing trials and filings, then scale reusable capabilities and standards that elevate decision quality across the hematology portfolio.

Essential Skills/Experience

Leadership & Influence

• Track record of translating complex analytical outputs into strategic recommendations that advise portfolio decisions.
• Experience working at the interface of Translational Science, Clinical Development, and Market Access functions, with the ability to distill technical complexity into clear narratives for diverse audiences.
• Demonstrated ability to lead without authority, influencing senior collaborators and cross-functional teams in a matrixed environment.

Technical and Strategic Expertise

• Expert command of oncology real-world data assets and their application to evidence generation.
• Proven track record to build end-to-end evidence strategies that address both clinical development needs (trial design, regulatory submissions) and market access needs (HTA requirements, payer value narratives).
• Strong foundation in evidence generation methodologies, including observational study design, causal inference, comparative effectiveness research, and ex