AI Lead, Manufacturing & Supply Chain

AstraZeneca•Boston, MA

15d•Hybrid

About The Position

Alexion is seeking a visionary AI lead to shape and execute the AI strategy across our manufacturing, quality, and supply chain operations. In this pivotal role, you will partner with global operations teams to turn complex production and quality challenges into intelligent, AI‑powered solutions that drive efficiency, enhance reliability, and ensure patients receive life‑changing therapies safely and on time. You will lead the design, development, and adoption of advanced AI/ML capabilities that optimize drug production, strengthen quality systems, streamline distribution, and enable data‑driven operational excellence. This role blends deep technical expertise with strong product thinking, manufacturing insight, and exceptional stakeholder leadership.

Requirements

Education: BS in Computer Science, Engineering, Industrial Engineering, Information Systems, or a relevant field required. MS or MBA preferred, with strong exposure to biopharma/biotech manufacturing, quality, and supply chain, and meaningful AI familiarity (coursework, certifications, executive programs, or applied initiatives).
Experience: 10+ years in biopharma or biotech Manufacturing, Quality, Technical Operations, or Supply Chain; 5+ years leading cross‑functional, technology‑enabled portfolios with measurable outcomes. Demonstrated success sponsoring/directing AI initiatives from concept to scaled adoption; not responsible for hands‑on coding or model development.
Business Domain Expertise: Deep understanding of GMP manufacturing, quality systems, tech ops, and end‑to‑end supply chain. Proven ability to translate operational needs into AI‑enabled solutions that improve reliability, cycle time, and cost.
Product and Portfolio Leadership: Proven ability to define roadmaps, prioritize portfolios, manage budgets, and deliver measurable impact.
Stakeholder Management: Credible partner to Plant Ops, Quality/QA, Supply Chain, Engineering, Procurement, Finance, Digital/IT, and Data/Tech; influences without authority and aligns internal and external partners.
Workflow Integration & Change: Track record embedding AI into MES, ERP, LIMS/ELN, QMS, APS, WMS, and logistics workflows; adept at change leadership, training, and benefits realization with human in the loop controls.
Data & Governance Fluency: Familiarity with key data domains (Manufacturing process and equipment, batch records, planning, QC and lab, etc.) and principles for quality, access controls, and lineage/traceability; experienced in vendor management.
Regulatory & Compliance: Working knowledge of GMP/GxP, validation (CSV/CSA), audit requirements, and documentation standards; familiarity with responsible AI and model risk management in GMP contexts.
Technical Fluency (non‑coding): Comfortable engaging with AI/ML concepts relevant to operations (predictive modeling, anomaly detection, forecasting and optimization, NLP for deviations and CAPA, computer vision for inspection). Able to assess value, risk, and feasibility; understand AI and platform considerations to make informed decisions and ask the right questions without needing to code or build models.
Communication & Storytelling: Exceptional ability to simplify AI concepts business audiences; craft outcome‑focused narratives and adoption cases; report progress, impact, and value to business and executive leadership with clear evidence and trade‑offs.

Nice To Haves

Rare disease or biologics manufacturing, including small batch operations, and specialized logistics.
Experience acting as business product owner for AI solutions, driving adoption and measurable outcomes.
Portfolio financial acumen (investment cases, ROI) and partnership governance (buy‑build‑partner, success measures, IP/publications stance)

Responsibilities

AI Strategy & Portfolio: Identify and prioritize high‑impact AI use cases in manufacturing, quality control, and supply chain optimization. Govern the portfolio from proof‑of‑concept through validation to scaled products, with clear stage gates, success metrics, and value tracking.
Ownership & Delivery: Act as the product owner for AI solutions (e.g., yield optimization, anomaly detection, batch release acceleration, demand and supply forecasting, inventory optimization, network planning, etc.). Set problem statements, adoption thresholds, and benefits realization plans; oversee delivery with internal teams and partners.
Business Interface & Partnerships: Serve as the primary interface between manufacturing, quality and supply chain teams, and external AI partners. Translate business needs into AI solutions. Evaluate buy‑build‑partner options and manage vendors/consortia.
Workflow Integration & Change: Lead cross‑functional integration of AI into MES, ERP, LIMS/ELN, QMS, APS, WMS, and logistics platforms, maintaining a strong focus on GMP/GxP compliance. Ensure AI‑driven insights are actionable in daily operations, supporting root cause analysis and continuous process improvement. Drive change management (stakeholder engagement, training/upskilling, super‑user networks, SOP/role updates, communications, feedback loops) to accelerate adoption and scale.
Data & Platforms: Partner with the Head of Data and tech teams to ensure data quality and secure integration across manufacturing and supply chain data sources. Influence platform choices and manage buy‑build‑partner decisions with clear success criteria and vendor governance.
Governance, GMP/GxP, and Responsible AI: Ensure adherence to GMP/GxP, data integrity (ALCOA+), validation (CSV/CSA), privacy, and audit requirements. Apply responsible AI practices including model validation, explainability, monitoring and drift management, change control, role‑based access, and inspection‑ready documentation.
Impact & Communication: Define OKRs/KPIs tied to business outcomes (e.g., uptime, efficiency/yield, batch release cycle time, deviation recurrence, schedule adherence, inventory turns, etc. ). Track and communicate progress, impact, and value to business teams and executive leadership with clear narratives and evidence.
Talent & Culture: Drive upskilling, change management, and a culture of AI adoption on the shop floor and in the supply chain. Build a cross‑functional network of super‑users and AI champions; set standards and best practices.