David leads McKinsey’s global scientific AI work, helping clients in the life sciences industry and beyond drive the next frontier of R&D productivity. This work covers a broad range of AI capabilities across biology, chemistry, materials, and physics. David brings together teams of scientific experts from McKinsey’s industry practices with deep technology expertise from QuantumBlack, AI by McKinsey to create strategies, blueprints, and roadmaps for the technology-driven transformation of product discovery and development processes in industries where science is at the core of innovation.
In pharma, where he spends most of his time, this means leveraging in silico methods—foundation models, machine learning, causal inference, knowledge graphs, and various modalities of scientific data across molecules, biological pathways, and patients—to expand the search space for discovery of new drugs, improve their properties through design and optimization, and develop the scientific parameters of clinical trials to improve their probability of success and reduce costs for sponsors and burden on patients.
In his client work, David focuses on:
- research acceleration: redesigning early stage discovery workflows using AI-driven hypothesis generation, active learning loops, and experiment automation to shrink design-make-test-analyze (DMTA) cycles
- development optimization: applying the latest advances in AI-powered surrogate modeling, trial simulation, and patient segmentation to de-risk pipelines, shorten timelines, and increase the probability of success
- next-gen evidence generation: leveraging the latest techniques in causal machine learning to build at-scale evidence-generation engines powering the full range of internal strategic decisions across functions and steady streams of external scientific publications on the safety and effectiveness of drugs
- rewiring of R&D domains: supporting clients with the full end-to-end reimagination of key domains of work, reengineering processes to embed and implement AI capabilities and their enablers across culture, operating model, data and tech architecture, governance, and talent
Before joining McKinsey, David obtained a PhD in electrical engineering from Princeton University, where his dissertation explored computer security architecture. He holds five US patents in system and processor security.