ProFound Therapeutics, Inc Boston, MA Senior​/Principal Scientist, Biostatistics & Computatio

Senior/Principal Scientist, Biostatistics & Computational Biology

Boston, MA USA

Position Summary

Pro Found Therapeutics is seeking a pioneering Senior Scientist/Principal Scientist, Biostatistics & Computational Biology to advance the Pro Foundry™ 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 Pro Foundry 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

Pro Found Therapeutics is a privately held, early‑stage biotechnology company founded by Flagship Pioneering, the creators of over 75 transformative companies including Moderna Therapeutics, Seres Therapeutics, and Indigo Agriculture. Pro Found is built on a foundation of scientific innovation and entrepreneurial spirit, with a mission to redefine the boundaries of human therapeutics.

Key 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.

Minimum Qualifications

• 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…