Vice President, Data, Analytics, AI – Technology

Responsibilities

• Develop and execute an enterprise digital strategy aligned to Rayze Bio's business objectives, driving operational efficiency, smarter decisions, and cross‑functional collaboration.
• Lead digital transformation initiatives that modernize data and reporting, streamline workflows, and accelerate execution through automation and AI/ML where it creates tangible business value.
• Identify and prioritize high‑impact opportunities to simplify the operating environment — reducing tool sprawl, minimizing friction, and improving speed without sacrificing quality or compliance.
• Own the enterprise analytics and AI/ML roadmap, prioritizing practical use cases across research, development, manufacturing, and commercial readiness.
• Oversee advanced analytics capabilities — including predictive modeling, forecasting, and machine learning — to support data‑driven decision‑making across functions.
• Establish fit‑for‑purpose data governance, ensuring data quality, integrity, security, and cross‑functional accessibility.
• Bring an external perspective on emerging technologies and translate them into pragmatic, adoption‑ready tools and approaches that Rayze Bio teams can and will use.
• Partner closely with Research and Drug Development leadership to leverage data, analytics, and AI across the drug development lifecycle — from discovery and translational science through clinical development and CMC.
• Improve how data is captured, integrated, and used across preclinical, clinical, and manufacturing interfaces to increase insight quality, reproducibility, and execution readiness.
• Enable practical analytics and AI use cases that support development execution — including trial planning, study performance visibility, and decision support — while remaining compliant and fit for purpose.
• Lead data and analytics capabilities supporting commercial readiness and launch execution, including forecasting, scenario modeling, and performance analytics aligned to demand, supply, and revenue planning.
• Enable commercial teams with the tools, infrastructure, and insights needed to support go‑to‑market execution, customer engagement, and post‑launch optimization.
• Partner closely with Commercial, Medical Affairs, Manufacturing, and Supply Chain to ensure analytic and technology readiness for launch and scaled commercialization.
• Own Rayze Bio's technology ecosystem — infrastructure, applications, collaboration tools, and data platforms — ensuring scalability, security, and compliance.
• Ensure technology decisions are practical and user‑centered — prioritizing adoption and real‑world utility over technical sophistication for its own sake.
• Partner with BMS IT, analytics, and data organizations to leverage enterprise platforms while ensuring solutions remain tailored to Rayze Bio's scale and operating model.
• Attract, develop, and retain top talent across data science, analytics, AI/ML, and technology; build a culture of innovation, ownership, and measurable impact.
• Lead effectively across a distributed, multi‑site organization — driving clear priorities, transparent communication, and strong execution rhythms.
• Serve as a member of the Rayze Bio leadership team; challenge assumptions, elevate thinking, and drive adoption of data‑driven and AI‑enabled ways of working across the organization.

• Bachelor’s degree required; advanced degree (MS, PhD, MBA) preferred depending on background and domain depth.
• 15+ years of progressive leadership experience across data/analytics, digital, AI/ML, and technology in biotech, pharma, life sciences, or similarly complex regulated environments.
• Senior leadership experience owning enterprise platforms, analytics strategy, and cross‑functional delivery across multiple sites.
• Strong understanding of the biotech/pharma value chain (research, drug development, manufacturing, commercialization); heavier research exposure preferred.
• Track record of delivering measurable business impact through analytics, digital enablement, and/or AI—beyond experimentation.
• Demonstrated ability to operate effectively within a larger enterprise ecosystem while tailoring solutions for a smaller, high‑speed subsidiary environment.

• data analytics
• AI
• machine learning
• predictive modeling
• forecasting
• automation
• digital strategy
• data governance
• data integration
• analytics capabilities

• leadership
• cross‑functional collaboration
• communication
• innovation
• decision‑making
• prioritization
• execution
• talent development
• problem‑solving
• adaptability

• Bachelor's degree
• Master's degree
• PhD
• MBA