Director, Team Lead, Computational Pathology Products (m/f/d)

Job description

Director, Team Lead, Computational Pathology Products (m/f/d) About AstraZeneca AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development and commercialization of prescription medicines for some of the world's most serious diseases. But we're more than one of the world's leading pharmaceutical companies. Site Description - Munich, Germany Welcome to Computational Pathology Munich, one of over 400 sites here at AstraZeneca, providing a collaborative environment where everyone can feel comfortable to be themselves - a value that is at the core of AstraZeneca's priorities. To help you maintain your best self, here's a sneak peek into some of the things we provide: after-work events, lunch & learns, a spacious and sustainable office working environment, events, family and childcare support and of course the Alps around the corner for hiking, biking and skiing. Position Summary The Director, Team Lead – Computational Pathology Products leads a cross-functional team of AI scientists and data managers to develop and deliver regulated computational pathology (CP) products for clinical use. This leader is accountable for driving the end-to-end product delivery lifecycle - from algorithm development and validation through to regulatory-grade deployment—ensuring compliant, reproducible, and clinically impactful AI-based pathology solutions. As an emerging leader in the AstraZeneca community, you will be a strategic thinker who influences across functions, providing leadership and guidance in achieving project goals. You will lead the development of quality-managed processes, interface with software development teams for tooling and platform requirements, align with data and pathology teams for data acquisition and ground truth generation, and collaborate with product management to ensure delivery of section objectives in line with business objectives. You will own performance development for your team while demonstrating leadership through internal consulting, mentoring, and active project support. Key Responsibilities Lead a cross-functional team of AI scientists and data managers delivering regulated computational pathology products for clinical use; ensure delivery of section objectives in line with business objectives; develop individuals within the team through coaching, mentoring, and career development. Lead the development and continuous improvement of quality-managed processes in partnership with Quality Management, ensuring compliance with IEC 62304, ISO 13485, ISO 14971, and AI/ML governance frameworks; enforce documentation rigor and audit readiness; proactively identify and address critical compliance risks. Act as a technical subject matter expert (SME) in establishing new partnerships with technology vendors, academic institutions, and clinical collaborators; support internal and external audits by providing technical evidence, process documentation, and expert representation of the team's capabilities and quality practices. Interface with software development teams to define tooling, platform, and infrastructure requirements for AI model training, validation, deployment, and workflow orchestration; translate scientific and product needs into clear technical specifications. Align with data and pathology teams to establish robust processes for data acquisition, annotation, and ground truth generation; ensure data quality, representativeness, and traceability standards required for regulated AI product development. Collaborate with product management to define product roadmaps, prioritize deliverables, and ensure alignment between scientific capabilities, regulatory requirements, and clinical end-user needs; make good decisions balancing science and business considerations. Contribute to functional scientific strategy by driving excellence in AI model development, validation methodologies, and reproducible MLOps practices for computational pathology algorithms; establish performance, fairness, and generalizability standards for AI in clinical contexts. Initiate and manage internal and external collaborations to access latest scientific developments and expand business opportunities; serve as key interface with external partners for clinical validation studies; conduct briefings and scientific meetings for internal and external representatives. Anticipate and manage project-related investigations and issues; engage internal and external experts to develop creative solutions to complex problems; identify and evaluate external opportunities and threats and influence strategy accordingly. Qualifications Required PhD (or equivalent) in computational science, machine learning, biomedical engineering, computational pathology, or a related quantitative discipline, with extensive relevant scientific experience. At least 10 years of experience in AI/ML development for medical or life sciences applications, with at least 5 years of proven experience leading cross-functional teams as a people manager. Expert knowledge in deep learning model development, training, and performance evaluation for medical image analysis, with a strong focus on computational pathology applications. Domain knowledge in Software as a Medical Device (SaMD), AI systems validation, and regulated product development; proven experience working with IEC 62304, ISO 13485, and ISO 14971. Experience supporting or reviewing regulatory submissions (FDA, EMA, IVDR) for AI-enabled diagnostics or SaMD products. Strong programming skills (Python, R, or equivalent) with proficiency in deep learning frameworks (PyTorch, TensorFlow) and standard development tools (git, CI/CD pipelines, project tracking). Expertise in scientific study design, planning, execution, and reporting, including validation study design for regulated AI products. Knowledge of data analysis and statistics applied to data distribution, imbalance, bias detection, and quality assessment for medical applications. Demonstrated ability to form and lead cross-functional teams, interfacing effectively with scientists of diverse backgrounds including pathologists, biologists, software engineers, statisticians, and quality professionals. Excellent written and oral communication skills in English, with demonstrated ability to explain complex scientific and technical concepts to diverse audiences. Preferred Prior experience of product development is a must, including Knowledge of AI system validation, including stability, explainability, and sub-group performance evaluation across heterogeneous data distributions (e.g., scanner variability, demographic subgroups). Familiarity with digital pathology formats, workflows, and imaging standards (DICOM, OME-TIFF, whole-slide imaging). Experience establishing external collaborations or partnerships with academic medical centers, technology vendors, or standards bodies. Experience with ground truth generation processes including pathologist annotation workflows, inter-observer variability assessment, and consensus methodologies. What you can expect: Individual development opportunities with a focus on lifelong learning Trust, appreciation, and room to shape things in a focused and passionate team Modern office space in Munich enabling collaborative, flexible, and agile work A diverse, inclusive, and bias-free work environment, actively welcoming applications from all qualified candidates, regardless of background or characteristics Date Posted 17-Aug.-2026 Closing Date AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.