Senior Director, Machine Learning & AI (BPD)

Strategy and portfolio

• Define andmaintainBPDs multi-year ML&AI strategy aligned withaPredictFirstCMCorganization the BPD digital transformation roadmap and AZs 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 graphsRAGand agentic architectures.

• Set practical engineering standards for the team including reproducibility model risk managementMLOps evaluationframeworksand human-in-the-loop approaches forGxP-adjacent use cases.

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

Team leadership

• Leadanddevelop ahigh-performingML&AI team of data scientists and AI/data engineers growing capability and reach through permanent hires secondments PDRAs and vendor partnerships.

• Create the operating modelownershipand 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 simplyuseit more.

Crossfunctional delivery

• Workwith modelling/AIdigitalizationand robotics transformation leads toaligninvestment dependencies and delivery plans across AIdataand automation.

• Partner with R&DITso enterprise platforms meet BPDs scientific needs and BPD requirements are visible in strategic platform roadmaps.

• Serve as BPDs 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 Developmentcolleaguesso 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 EnterpriseAIand R&D IT to connect ML&AI models agents and decision-support tools with laboratory automationinstrumentationand closed-loop experimental workflows.

Governancecomplianceand risk

• Ensure BPDs AI work aligns with AZ AI governance data governance informationsecurityandGxPexpectations as well as emerging external regulatory guidance on AI in CMC.

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

• Be accountable forresponsible-AIpractice across the BPD portfolio including model documentation validation evidence bias and robustness testing and lifecycle management.

External innovation and partnerships

• Work with the AI PartnershipsLead to bring useful external thinking into BPD through academic collaborationsconsortiaand vendor evaluations.

• Represent BPD externally through selected publicationsconferencesand standards forums where this supports the strategy.

Stakeholder engagement

• Briefdigital transformation andBPDleadershipon progress valuetrade-offsand risk distinguishing clearly between proven capability activepilotsand speculative opportunities.

• Act as a trusted advisor to BPD functionalleaders onwhere AI can and cannot help them meet theirobjectives.

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.TypicallyPhD plus 7 years relevant experience or MSc plus 10 years relevant experience.

• Track recordof 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 andMLOps. The expectation is not deepexpertisein 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 modeanalysisandappropriate controlsfor scientific use.

• Strong understanding ofproductionML and AI engineering including reproducible development version control testing CI/CDcontainerizeddeployment monitoring model lifecyclemanagementand operational support.

• Experienceestablishingpractical 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 manufacturingscienceor regulatory submissions.

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

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

• Experience withGxP-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 queryapproachesor semantic architectures that support knowledge management and reuse.

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

• Experience scaling AI tools for use by non-technical scientific staff including adoption trainingfeedbackand support models.

• Peer-reviewed publications patents open-source contributions or visibleexternal contributions in applied MLAIor data sciencefor life sciences.

What success looks like in the first 1218 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 aprioritizedunit operation with a defensible modelling strategy for the rest of the roadmap.

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

• BPD requirements reflected in enterprise roadmaps deliverycommitmentsand platform investment decisions.

The annual base pay for this position ranges from $203213.60 - $304820.40 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 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 tableFantastic! We have one seat available and we hope its 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.