Senior Director, Digital & Computational Pathology

About the Role

The Senior Director, Digital and Computational Pathology (DPCP) will define and lead AstraZeneca’s end-to-end strategy for DPCP image analytics, bridging R&D innovation with Commercial execution. The role focuses on building scientifically credible, compliant, and scalable solutions while developing clear go‑to‑market (GTM) models that translate imaging science into differentiated products, services, and partnerships. You will steward a cross‑functional team integrating pathology, AI/ML, software engineering, and product management, partnering closely with Discovery, Translational Medicine, Clinical Development, Diagnostics, IT, and Commercial Franchises to deliver decision‑changing insights and market‑ready offerings.

• Strategy and Vision
  :
  Set a multi‑year vision that connects scientific roadmaps with commercial opportunity, defining DPCP pathways, value propositions, and portfolio alignment across therapy areas.
• GTM Leadership
  :
  Design and execute GTM strategies for DPCP solutions, including segmentation, and commercialization models.
• R&D Partnership
  :
  Translate discovery and translational advances (e.g., image‑based biomarkers, spatial analytics) into validated offerings; align on clinical utility, evidence generation, and regulatory strategy with therapy area teams.
• Commercial Franchise Collaboration
  :
  Co‑create customer narratives, market requirements, and adoption programs with Franchises; build field enablement materials, and define success metrics (penetration, utilization, outcomes impact).
• Product Management
  :
  Own product life cycle for digital pathology capabilities—concept, MVP, validation, launch, and scale—ensuring user‑centric design, compliance, and measurable value.
• Platform Ownership
  :
  Lead enterprise‑grade platform validation (WSI ingestion, storage/rendering, annotation, algorithm deployment, audit trails), ensuring security, interoperability, and performance at scale.
• AI/ML Development
  :
  Oversee validation of algorithms (classification, segmentation, cell phenotyping, spatial fusion) with robust MLOps, monitoring, and continuous improvement.
• Evidence and Outcomes
  :
  Define evidence plans that demonstrate analytical validity, workflow efficiency, and economic value; partner on HEOR and real‑world performance studies.
• Regulatory and Quality
  :
  Follow regulatory pathways (GxP/IVD considerations), quality systems, and readiness for image‑based biomarkers and diagnostics; engage with regulators and standards bodies as needed.
• Partnerships and Ecosystem
  :
  Structure strategic collaborations with scanner vendors, CROs, diagnostics partners, health systems, and technology providers; negotiate commercial terms and co‑development agreements.
• Operations and Scale
  :
  Build scalable, compliant workflows from sample receipt through imaging, QC, analysis, reporting, and knowledge capture; optimize throughput, turnaround time, and cost.
• Commercial Enablement
  :
  Develop training, sales tools, messaging, and playbooks; support customer pilots, proof‑of‑value programs, and post‑launch adoption/expansion.
• Communication and Change Management
  :
  Evangelize capabilities across AZ and with external stakeholders; communicate impact to senior leadership with clear metrics and narratives.

• Education
  :
  PhD, MD, or equivalent experience in Pathology, Biomedical Engineering, Computer Science, Data Science, or related field.
• Experience
  : 5+ years in digital pathology, computational imaging, or applied AI/ML in biopharma/healthcare; 5+ years leading multidisciplinary teams and product portfolios at senior level.
• Domain Expertise
  :
  Deep knowledge of histopathology workflows, multiplex imaging (IHC/IF, mIHC, IMC, spatial transcriptomics), WSI formats, and image analysis methods.
• GTM and Productization
  :
  Proven track record building and commercializing digital health or diagnostics solutions, including pricing, packaging, channel strategy, and launch execution.
• Technical Skills
  :
  Experience with deep learning, classical image analysis, spatial statistics, model validation, MLOps, and deployment at scale.
• Regulatory and Quality
  :
  Experience with biomarker validation, analytical/clinical…