AI PROGRAM IMPLEMENTATION LEAD – LAB OPERATIONS

This role focuses on scaling hyperautomation and AI-driven transformation within Lab Operations. The candidate will identify, analyze, and deliver high-impact automation use cases leveraging AI, GenAI, RPA, and Data Analytics, ensuring a structured roadmap, actionable business cases, and measurable return on investment (ROI).

REQUIRED QUALIFICATIONS

• Hands-on experience across AI, Data, RPA, and GenAI, with a broad understanding rather than specialization in a single area.

EXPERIENCE MANAGING A LARGE CROSS-FUNCTIONAL PROGRAM IMPLEMENTATION IN AN ORGANIZATION WITH MULTIPLE STAKEHOLDERS

• Experience in business analysis, process improvement, or automation within large organizations.
• 2-3 years of experience in Biotech, Healthcare, or Life Sciences, with a focus on AI, Data, and Automation programs.

CORE COMPETENCIES & SKILLS

• Industry Knowledge – Familiarity with operational processes within Lab Operations.
• Automation & Analytics Tools – Experience with AI/ML tools and frameworks.
• Data-Driven Decision Making – Ability to perform exploratory data analysis (EDA) and “What If” scenarios to drive automation insights.

• Use Case Identification & Prioritization – Collaborate with Lab Operations, Engineering and Product teams to analyze business processes and identify inefficiencies and areas suitable for automation.
• Automation Strategy & Roadmap – Develop and execute short- and long-term automation roadmaps aligned with business objectives in coordination with Lab Operations and Engineering.
• Market exploration - Identify, assess and partner with AI-driven operation improvement companies that align with Lab Operations AI strategy including AI technology assessment and risk evaluations.
• Stakeholder Engagement – Secure leadership buy-in through well-defined business cases demonstrating clear value.
• Solution Design & Documentation – Collaborate with Engineering and operations teams and other stakeholders to define automation requirements aligning with regulatory compliance and industry standards.
• Implementation program management - Lead implementation of the AI automation projects.
• ROI & Impact Analysis – Conduct ROI simulations and monitor post-implementation performance to measure business impact.
• Workshops & Proof of Value (POV) Development – Organize automation idea sessions to showcase AI/RPA capabilities and explore new applications.
• Risk Management & Compliance – Ensure all automation initiatives comply with industry regulations, including FDA, ISO, and GMP standards.