Senior Director, Machine Learning & AI (BPD)

Strategy and portfolio 

• Define and maintain BPD’s multi-year ML&AI strategy, aligned with a Predict‑First CMC organization, 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, digitalization 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 such as computer science, machine learning, statistics, applied mathematics, physics, computational biology, chemical or biochemical engineering, or a closely related field. Typically, PhD plus 7 years’ relevant experience, or MSc plus 10 years’ relevant experience. 

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

• Strong technical judgement across modern ML and AI, including classical ML, deep learning, foundation models, LLMs, RAG, agentic AI, knowledge graphs, digital twins and MLOps. The expectation is not deep expertise in every area, but sufficient technical depth to guide architecture, challenge assumptions and make sound delivery decisions. 

• Experience shaping LLM, RAG or agent-based solutions from problem definition through architecture, evaluation and deployment, including retrieval design, grounding, human review, failure mode analysis and appropriate controls for scientific use. 

• Strong understanding of production ML and AI engineering, including reproducible development, version control, testing, CI/CD, containerized deployment, monitoring, model lifecycle management and operational support. 

• Experience establishing practical evaluation approaches for ML and AI systems, including benchmarks, test datasets, model performance measures, uncertainty, robustness, explainability and user feedback loops. 

Desirable 

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

• Experience applying ML or AI to complex scientific, engineering or industrial problems, rather than only general business analytics or consumer-facing applications. 

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

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

• Familiarity with enterprise search, graph-based retrieval, graph query approaches or semantic architectures that support knowledge management and reuse. 

• Familiarity with hybrid mechanistic-ML modelling, Bayesian methods, Gaussian Processes, active learning, Bayesian optimization or digital twins relevant to process development or manufacturing. 

• Experience scaling AI tools for use by non-technical scientific staff, including adoption, training, feedback and support models. 

• Peer-reviewed publications, patents, open-source contributions or visible external contributions in applied ML, AI or data science 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 prioritized 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. 

The annual base pay for this position ranges from $203,213.60 - $304,820.40 USD Annual. Hourly and salaried non-exempt employees will also be paid overtime pay when working qualifying overtime hours. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program (salaried roles), to receive a retirement contribution (hourly roles), and commission payment eligibility (sales roles). Benefits offered included a qualified retirement program [401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans. Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

Are you ready to bring new insights and fresh thinking to the table? Fantastic! We have one seat available, and we hope it’s yours. Apply today.

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 follow all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.