• Design AI-based systems that: • Explore UI, APIs, workflows, and edge cases without pre-defined scripts • Generate and execute tests dynamically from product behavior • Discover bugs through exploration, not just assertions • Move beyond static automation into self-directed testing systems • Build QA agents that: • Continuously test features as development agents build them • Provide structured, actionable feedback in real time • Loop with development agents until quality thresholds are met • Own the QA feedback loop as a first-class system, not a manual process • Validate quality across: • Web UI • Backend services • APIs • Data integrity • End-to-end user journeys • Ensure the system works as a cohesive product, not just isolated components • Automatically identify: • Functional defects • Workflow breaks • Regression issues • Performance and reliability risks • Ensure issues are: • Reproducible • Verified • Clearly explainable to humans • QA does not stop at “test execution” • Build systems that: • Run continuously • Adapt as the product evolves • Increase coverage automatically over time • Define what “release-ready” means • Block releases when quality is insufficient — with evidence • Raise risks early, clearly, and decisively

• Oversee the end‑to‑end quality program for an assigned product value chain in antibody drug conjugates. • Be a core member of the Value Chain Management Team, partnering across functions to ensure a reliable supply of high‑quality products throughout their commercial lifecycle. • Lead end‑to‑end quality oversight for the product program, ensuring compliance with procedures and regulations. • Develop and maintain the Product Quality Strategy and Product Analytical Strategy in line with supply strategy. • Partner with Quality, Regulatory, Technical, Supply Chain, and site Quality teams to align priorities and resolve issues. • Strengthen and harmonize Quality Management Systems and ways of working, sharing best practices across sites. • Stay current with regulatory requirements, support or lead inspections, and drive ongoing inspection readiness. • Provide quality leadership to product focus teams, the Value Chain Management Team, and related working groups. • Lead quality communications across the network, including meetings, updates, and escalation of issues and risks. • Support analytical siting strategies and broader value‑chain projects, ensuring quality input and follow‑through. • Track and interpret change control, complaints, and quality performance data to identify trends. • Review Annual Product Reviews, evaluations, and post‑marketing product reviews to assess and communicate risks. • Monitor, report and mitigate quality and supply chain risks across the franchise.

• Provide day-to-day leadership, oversight, and performance management for a team of QA Analysts • Define and implement a strategy for AI-assisted manual testing that enhances exploratory depth, risk discovery, and test design efficiency • Enable QA Analysts to leverage AI tools for test case ideation, scenario expansion, regression gap analysis, and defect clarity, while maintaining human accountability for validation decisions • Set clear priorities, ensure accountability, and maintain consistent execution standards across projects • Lead risk-based test planning to ensure critical workflows and customer-impacting functionality are thoroughly validated • Oversee release readiness activities, including test strategy alignment, defect triage, and quality sign-off • Represent QA in cross-functional planning, refinement, and product discussions • Partner with SDET and Engineering leaders to identify automation opportunities and improve overall test strategy • Analyze defect trends and quality metrics to drive measurable process improvements • Champion the customer experience by advocating for usability, reliability, and product integrity

• Lead and mentor a team of SDETs and automation engineers, setting clear standards for technical excellence and delivery • Drive the strategy and implementation of AI-powered and agentic automation solutions to improve defect detection, test generation, test maintenance, and deployment validation • Evaluate and integrate emerging AI tooling into the quality engineering ecosystem, ensuring scalable, secure, and measurable impact on release confidence and automation efficiency • Lead and monitor reduction of change failure rates through improved test architecture, release validation, and deployment safeguards • Expand and optimize automated test coverage across UI, API, integration, and backend systems • Partner with cross-functional stakeholders to proactively identify risk and improve release stability • Champion continuous improvement through root cause analysis, defect trend evaluation, and performance monitoring • Remain hands-on when necessary, reviewing automation design, validating technical approaches, and guiding architectural decisions • Drive continuous improvement by identifying gaps in processes, tooling, and test coverage