Success in this role will require enterprise leadership: developing leaders and senior scientists, building an inclusive and high-accountability culture, partnering deeply across Protein Sciences, Prototyping, Technology, Data Sciences, Machine Learning, Biology, and CMC/Tech Ops, and translating an ambitious scientific vision into robust capabilities that improve decision quality, speed, and scale. The ideal candidate brings deep technical breadth, a quantitative mindset, and a clear point of view on how pooled assays, DNA/barcode-enabled readouts, synthetic biology, automation, microfluidics, advanced detection, and computational modeling can transform how protein therapeutics are evaluated.