Better pharmaceutical decisions under uncertainty.
My perspective on pharmaceutical strategy is shaped by leadership experience in global drug development programmes and enterprise analytics, a foundation in quantitative sciences, and doctoral research in decision science.
This combination has shaped a question that runs through my work. How do we make strategic decisions that remain defensible when evidence is incomplete, priorities compete, and outcomes are uncertain?
18 years across four global pharmaceutical companies. MBA, University of St. Gallen. Doctoral examination passed, ETH Zurich (September 2026); degree conferral pending.
Research-based perspectives on the questions shaping pharmaceutical strategy, portfolio decisions, artificial intelligence, and organizational transformation.
Original research. Transparent methods. Defensible conclusions.
Doctoral examination passed at ETH Zürich (September 2026); degree conferral pending.
Output
Singh P, et al. Integrating Sustainability Pillars Into Trial Design Decision-Making: Results of an International Survey. Clinical and Translational Science. 2025;18(5):e70241. doi:10.1111/cts.70241
Singh P, et al. Design and Execution of Sustainable Decentralized Clinical Trials. Clinical Pharmacology & Therapeutics. 2023;114(4):802–809. doi:10.1002/cpt.3009
The Decision Lab is a developing initiative that brings research, strategy, and simulation into the same conversation. It explores how leaders navigate uncertainty, competing priorities, and strategic trade-offs when making decisions that shape organizational direction, resource allocation, and long-term value creation. Quantitative methods and computational approaches help make underlying assumptions visible, challenge conventional thinking, and illuminate the consequences of alternative strategic choices.
The Decision Lab is developing illustrative models and decision-support methods for research and educational purposes. Demonstrations will be released following methodological and technical review, with assumptions, interpretations, and limitations made explicit.
ILLUSTRATIVE · INVENTED INPUTS · NOT A VALIDATED RESULTQuestion: which one project to fund, if only one can be funded? Alternatives: three projects. Constraints: one funding slot; a single uncertain assumption. Interpretation: below about 42% assumed success Project 2 is preferred, above it Project 1 is, so the discussion should be about that assumption rather than the ranking. Limitations: all numbers are invented to explain the idea; no real portfolio or method is implied.Explore the Decision Lab
Academy
Learning Through Evidence and Application
Exploring practical approaches to decision science, pharmaceutical strategy, quantitative analysis, and emerging technologies.
