Senior Director, Machine Learning & AI (BPD)

Role purpose 

AstraZeneca's bold ambition is to be a pioneer in science, lead in our disease areas and transform patient outcomes — and by 2030, to deliver 20 new medicines and industry‑leading growth. Biologics are central to that ambition, and Biopharmaceutical Development (BPD) is the R&D function that turns biologic candidates into medicines. BPD develops the cell lines, bioprocesses, formulations, devices and analytical methods needed to advance biologic medicines through clinical development and approval where they can improve the lives of patients. As the portfolio grows in scale and complexity, BPD is increasingly adopting a Predict‑First CMC approach: FAIR data at source, greater use of modelling and digital twins, and AI-enabled tools that help scientists find knowledge, make decisions and create regulatory content more efficiently. 

The Senior Director, Machine Learning & AI leads the ML & AI team within BPD: a multidisciplinary group of specialists spanning data science, AI and data engineering, and applied machine learning research. The role is accountable for translating BPD's Predict First ambition into a coherent AI strategy and portfolio roadmap that transforms emerging technologies and promising ideas into trusted, scalable capabilities that deliver measurable scientific and business value. The Director defines the ML & AI strategy for BPD, owns delivery of the AI portfolio within the digital transformation roadmap, and serves as BPD's senior technical interface with Enterprise AI and R&D IT. The role is responsible for establishing a framework that rapidly tests and demonstrates value through proof-of-concepts (PoCs), accelerates adoption through iterative delivery, and enables the scaling of successful AI solutions across BPD. 

In addition, the Director partners closely with Robotics & Automation, Informatics, Digital Transformation, Enterprise AI, and R&D IT teams to identify opportunities where ML & AI can enhance scientific, operational, and business outcomes and to integrate AI capabilities into products, platforms, and workflows across BPD (e.g. Physical AI). The role provides strategic leadership on the data foundations required to enable AI at scale, including data architecture, governance, engineering, and platform capabilities, ensuring that high-quality, accessible, and trusted data can support advanced analytics, machine learning, and AI solutions across the enterprise. 

Success in this role requires a balance of strategic leadership and technical credibility. The Director will shape investment decisions, build organisational capability, drive adoption across BPD, influence senior stakeholders across BPD and the enterprise, and provide the technical judgement needed to guide delivery and manage risk. 

Key accountabilities 

Strategy and portfolio 

• Define and maintain BPD’s multi-year ML&AI strategy, aligned with a Predict‑First CMC organisation, the BPD digital transformation roadmap and AZ’s AI30 ambitions. 

• Be accountable for the BPD AI portfolio across the four pillars: AI Foundations & Platforms, Knowledge Management, Modelling & Digital Twins, and Submission & Report Authoring. 

• Set portfolio priorities across in-flight, self-funded and proposed initiatives, making clear, evidence-based recommendations on when to build, buy, partner, pause or stop. 

Technical leadership 

• Provide senior technical oversight of model strategy, evaluation and deployment across predictive ML, mechanistic and hybrid models, protein sequence and structure models, knowledge graphs, RAG and agentic architectures. 

• Set practical engineering standards for the team, including reproducibility, model risk management, MLOps, evaluation frameworks and human-in-the-loop approaches for GxP-adjacent use cases. 

• Chair or lead technical review of the highest-risk or highest-value deliverables, ensuring decisions are well evidenced and risks are visible to the right governance forums. 

Team leadership 

• Lead and develop  a high-performing ML&AI team of data scientists and AI/data engineers, growing capability and reach through permanent hires, secondments, PDRAs and vendor partnerships. 

• Create the operating model, ownership and delivery discipline needed for a small specialist team to have enterprise-level impact. 

• Support AI training and culture change across BPD, helping scientists use AI well rather than simply use it more. 

Cross‑functional delivery 

• Work with modelling/AI, digitalisation and robotics transformation leads to align investment, dependencies and delivery plans across AI, data and automation. 

• Partner with R&D IT so enterprise platforms meet BPD’s scientific needs, and BPD requirements are visible in strategic platform roadmaps. 

• Serve as BPD’s senior technical voice into Enterprise AI: adopt enterprise capability where it fits, escalate gaps, and shape shared offerings where BPD should not rebuild common capability 

• Work closely with CMC Statistics, Informatics & Software Engineering, and Robotics & Automation Development colleagues so that ML&AI outputs sit on sound statistical, software and laboratory foundations.Build Physical AI as an emerging BPD capability by partnering with Robotics & Automation, Informatics, Digital Transformation, Enterprise AI and R&D IT to connect ML&AI models, agents and decision-support tools with laboratory automation, instrumentation and closed-loop experimental workflows. 

Governance, compliance and risk 

• Ensure BPD’s AI work aligns with AZ AI governance, data governance, information security and GxP expectations, as well as emerging external regulatory guidance on AI in CMC. 

• Contribute to AZ's regulatory advocacy on AI in CMC where BPD's experience is directly relevant (e.g. via the CMC Strategy Board and PMF AI in CMC Working Group). 

• Be accountable for responsible-AI practice across the BPD portfolio, including model documentation, validation evidence, bias and robustness testing, and lifecycle management. 

External innovation and partnerships 

• Work with the AI Partnerships lead to bring useful external thinking into BPD through academic collaborations, consortia and vendor evaluations. 

• Represent BPD externally through selected publications, conferences and standards forums where this supports the strategy. 

Stakeholder engagement 

• Brief digital transformation and BPD leadership on progress, value, trade-offs and risk, distinguishing clearly between proven capability, active pilots and speculative opportunities. 

• Act as a trusted advisor to BPD functional leaders on where AI can, and cannot, help them meet their objectives. 

Qualifications and experience 

Essential 

• Advanced degree (MSc or PhD) in a quantitative discipline: computer science, machine learning, statistics, applied mathematics, physics, computational biology, chemical/biochemical engineering, or a closely related field. PhD plus 7 yr of relevant experience. MSc plus 10 ye of experience. 

• Track record of leading ML&AI teams that deliver production capability, not just prototypes, in regulated or scientifically demanding environments. 

• Deep, current, hands-on knowledge across the following: classical ML, deep learning, foundation or language models, agentic systems, digital twins, knowledge graphs, RAG and MLOps. 

• Strong software engineering discipline; fluent in Python and modern ML tooling; comfortable working in cloud environments such as Azure or AWS, including containerised workloads and distributed compute. 

• Experience turning ambiguous scientific or business problems into shaped AI solutions, including knowing when AI is not the right answer. 

• Ability to influence senior stakeholders across scientific, technical and business functions, and to make clear recommendations under uncertainty. 

• Experience building durable partnerships with IT/platform teams, external vendors and academic groups, with clear commercial, technical and delivery outcomes. 

Desirable 

• Domain understanding of biologics CMC, bioprocess development, formulation, analytical development, or regulatory submissions. 

• Familiarity with FAIR data principles, data product thinking, ontologies and knowledge graphs applied to scientific data. 

• Experience with GxP‑adjacent AI, model validation for regulated use, or contribution to regulatory advocacy on AI/ML. 

• Peer-reviewed publications or recognized external contributions in applied ML for life sciences. 

What success looks like in the first 12–18 months 

• Measurable time saved on knowledge retrieval across BPD, supported by an agent architecture and evaluation framework the team is confident to scale. 

• At least one authoring pipeline moved from proof of concept into production use for a regulatory submission or comparability report. 

• A working digital twin capability for a prioritised unit operation, with a defensible modelling strategy for the rest of the roadmap. 

• An ML&AI team that is known — inside BPD and beyond — for high‑quality delivery, clear technical judgement and honest communication about what AI can and cannot do. 

• BPD requirements reflected in enterprise roadmaps, delivery commitments and platform investment decisions.