Associate Director, Portfolio Modeling & Simulation

Who We Are

Flagship Pioneering is a bioplatform innovation company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

Position summary

We are seeking an Associate Director, Portfolio Modeling & Simulation to join the Strategic Operations and Analytics team to collaborate with Pioneering Intelligence (PI), a Flagship initiative dedicated to developing AI/ML-powered platforms for transformative applications in life sciences and related fields, to build a state-of-the-art computational capabilities for strategic portfolio modeling. Flagship Pioneering increasingly leverages data and systems to create novel insights that drive some of the most important decisions made across the Flagship enterprise. The Strategic Operations and Analytics team works closely with Flagship’s CEO, Managing Partners, and most teams across Flagship in support of driving value throughout the organization and across our companies. In this role, you will lead the design and implementation of quantitative models that guide portfolio strategy across a complex pipeline. You will advance the science and ship decision tools that translate research into strategic resourcing and allocation choices for high-impact decisions across the Flagship ecosystem by building proprietary models and leveraging state-of-the-art computational methods. 

Responsibilities

• Formulate and solve multi-objective portfolio optimization problems using modern optimization libraries and state-of-the-art statistical methods; generate Pareto trade-offs and scenario recommendations.
• Develop and validate PTRS models; calibrate against internal/external benchmarks and quantify uncertainty.
• Build portfolio simulations (e.g., Monte Carlo, scenario/sensitivity, discrete-event) to quantify risk-adjusted value, timelines, and resource needs
• Incorporate asset interdependencies (correlated technical risk, shared resources, indication overlap) and real-world constraints into models
• Design decision frameworks to inform asset selection, staging, and go/no-go decisions
• Build and maintain data pipelines; ensure data quality and documentation
• Create visualizations for translating complex results into key insights 
• Collaborate cross-functionally to develop, validate, and refine models
• Present progress in regular research meetings, prepare reports, and present findings for stakeholders and leadership
• Stay up to date on advances in Bayesian modeling, stochastic/robust optimization, and portfolio analytics; propose and prototype improvements

Qualifications

• PhD in a highly quantitative field (Statistics, Applied Math, Operations Research, Computer Science, Econometrics, Biostatistics, or similar).
• Deep proficiency in probabilistic/Bayesian modeling and uncertainty quantification (hierarchical models, survival/time-to-event, calibration, posterior predictive checks).
• Hands-on simulation experience (Monte Carlo, scenario/stress testing, discrete-event) plus sensitivity/uncertainty analysis.
• Decision/valuation modeling for R&D portfolios (risk-adjusted NPV, VOI/EVSI, real options); ability to translate business questions into quantitative experiments.
• Fluency in Python and standard statistical analysis packages
• Demonstrated ability to collaborate in interdisciplinary teams and communicate complex technical concepts effectively
• Ability to take initiative and work independently