Senior/Principal Scientist, Biostatistics & Computational Biology

Flagship Pioneering•Boston, MA

13d

About The Position

ProFound Therapeutics is seeking a pioneering Senior Scientist/Principal Scientist, Biostatistics & Computational Biology to advance the ProFoundry™ Platform through rigorous statistical modeling, inference, and translational analytics. This role is ideal for a candidate with deep expertise in biostatistics, theoretical statistics , and quantitative data analysis, who is passionate about applying statistical principles to complex biological data relevant to cardiometabolic or neurodegenerative disorders to drive therapeutic discovery. The ProFoundry Atlas—a proprietary resource of genetic, transcriptomic, proteomic, and imaging data—offers a unique opportunity to uncover novel biological insights and prioritize targets for drug development. The successful candidate will play a central role in developing statistical frameworks and analytical pipelines that support preclinical research, target validation, and portfolio decision-making. Company Summary ProFound Therapeutics is a privately held, early-stage biotechnology company founded by Flagship Pioneering , the creators of over 75 transformative companies including Moderna Therapeutics (NASDAQ: MRNA), Seres Therapeutics (MCRB), and Indigo Agriculture. ProFound is built on a foundation of scientific innovation and entrepreneurial spirit, with a mission to redefine the boundaries of human therapeutics.

Requirements

PhD in Biostatistics, Statistics, Applied Mathematics , or a related quantitative field with 3+ years of postdoctoral or industry experience; or MS with 7+ years of relevant experience
Strong foundation in statistical theory, including probability, inference, regression, and multivariate analysis
Experience applying statistical methods to biological or biomedical datasets, including genomics, transcriptomics, or clinical data relevant to cardiometabolic/neurodegenerative disorders
Proficiency in statistical programming languages (e.g., R, Python) and familiarity with statistical computing environments
Demonstrated ability to develop and validate statistical models and algorithms for complex, high-dimensional data
Commitment to reproducible research and collaborative problem-solving
Excellent communication skills, with the ability to explain statistical concepts to non-statistical audiences

Nice To Haves

Expertise in Bayesian statistics , hierarchical modeling, and probabilistic graphical models
Experience with longitudinal data analysis , survival analysis, and time-to-event modeling in clinical or biomedical contexts relevant to cardiometabolic/neurodegenerative disorders
Familiarity with causal inference frameworks , such as propensity score methods, instrumental variables, or structural equation modeling
Knowledge of experimental design , including randomized trials, adaptive designs, and observational study methodologies
Experience with dimension reduction techniques (e.g., factor analysis, t-SNE, UMAP) and high-dimensional inference
Exposure to functional data analysis , spatial statistics, or statistical methods for imaging data
Familiarity with meta-analysis , statistical harmonization, and federated data analysis across cohorts
Experience with statistical consulting or collaboration in multidisciplinary teams, especially in biomedical or pharmaceutical settings
Knowledge of regulatory standards and statistical considerations in drug development (e.g., FDA guidelines, ICH E9/E10)
Experience with statistical software development , package creation in R/Python, or contributions to open-source statistical tools
Familiarity with cloud-based statistical workflows and scalable computing (e.g., parallel processing, distributed computing frameworks)
Experience in preclinical biostatistics , including statistical design and analysis of in vitro and in vivo studies
Familiarity with dose-response modeling , pharmacokinetic/pharmacodynamic (PK/PD) modeling, and biomarker evaluation
Knowledge of target validation pipelines , including CRISPR screens, RNAi, and functional genomics
Exposure to portfolio analytics and decision-support tools used in early drug discovery
Understanding of statistical considerations in translational research , including bridging preclinical and clinical data
Experience with model-based prioritization frameworks , such as multi-criteria decision analysis (MCDA) or machine learning-based scoring systems
Ability to communicate statistical findings in the context of biological relevance and therapeutic strategy

Responsibilities

Design and implement statistical models for analyzing high-dimensional biological data, including genomics, transcriptomics, proteomics, and imaging relevant to cardiometabolic or neurodegenerative disorders
Apply principles of theoretical and applied statistics to develop novel inference methods tailored to biological questions
Lead the development of robust, reproducible pipelines for hypothesis testing, causal inference, and predictive modeling across multi-omics datasets
Collaborate with interdisciplinary teams to translate statistical findings into biological insights and therapeutic hypotheses
Develop simulation frameworks and statistical benchmarking tools to evaluate model performance and data quality
Apply advanced statistical modeling to support target discovery, prioritization, and validation , integrating multi-omics and functional data
Design and analyze preclinical experiments , including dose-response studies, biomarker discovery, and mechanistic investigations
Use Bayesian and frequentist frameworks to quantify uncertainty and support decision-making in early-stage drug development
Build simulation models to assess study designs, optimize resource allocation, and forecast outcomes in preclinical pipelines
Contribute to portfolio-level analytics , helping prioritize targets based on statistical evidence, biological plausibility, and translational potential
Support statistical planning for in vitro and in vivo studies, including power calculations, randomization schemes, and reproducibility assessments
Develop scoring systems and prioritization frameworks for ranking therapeutic targets using multi-dimensional data inputs
Ensure statistical rigor in the interpretation of experimental results, guiding go/no-go decisions and milestone reviews