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(Scientist) Sr. Scientist, Cardiovascular and Metabolic Disease

Flagship Pioneering, Inc.·US·Cambridge, MA USA·senior
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Company Summary ProFound™ Therapeutics is a biotechnology company discovering novel proteins across the expanded human proteome to unlock first-in-class medicines and transformative drug targets. The company’s proprietary ProFoundry™ Platform integrates state-of-the-art protein detection, validation, and computational technologies to systematically identify and characterize previously inaccessible proteins and validate their therapeutic potential. This approach has generated a rapidly expanding portfolio of novel targets and programs advancing toward the clinic, along with strategic collaborations with leading pharmaceutical partners. For more information, please visit www.profoundtx.com. Position Summary We are seeking an exceptional Scientist or Senior Scientist to lead and support discovery efforts in projects in Cardiovascular and Metabolic Disease. This role will contribute to shaping our pipeline, bringing scientific leadership and creativity to target validation, drug discovery translational biology. The ideal candidate brings a strong foundation in cardiovascular or metabolic disease biology, a passion for novel biology, and experience designing and executing mechanistic and pharmacology studies that bridge in vitro and in vivo models This is a high-impact, cross-functional role with the opportunity to work closely with platform scientists, computational biologists, and drug discovery teams to identify and advance novel targets into drug development. Key Responsibilities Drive discovery research in cardiovascular and/or metabolic disease areas by designing, executing, and interpreting mechanistic studies using state-of-the-art models and technologies Lead target validation efforts informed by proteomic and computational insights; develop hypotheses and prioritize targets with high translational potential Collaborate with computational biology and proteomics teams to interpret large-scale datasets (e.g., proteomic, transcriptomic, genetic) and generate novel