Careers

At Applied BioMath, our passion for science and technology is what drives our desire to revolutionize drug invention. Our team members are innovators and entrepreneurs at heart, and enjoy pioneering this paradigm shift of how drug invention is done. We love to learn, challenge ourselves and others, and create new science.

Team work, respect, and trust are essential to Applied BioMath’s daily culture. These three values are, and will continue to be, the three pillars on which we build our ABM team. We work as a team, within our company, amongst our peers, and with our computational partners.

* If you are looking to work in an environment that supports exploration, pushing the envelope, and taking risk…
* If you are ready to make a difference in science…
* If you find yourself casually conversing about eigenvectors, kinases, or programming with friends….

… Then Applied BioMath is the place for you.

Current Open Positions

  • Principal Scientist, QSP Model Lead

    Applied BioMath is revolutionizing drug invention by helping partners accelerate best in class therapeutics into the clinic. We do this by integrating disease mechanisms, therapeutic mechanism of action, rigorous mathematics, high performance computing and systems pharmacology approaches. Our analyses have assisted both large and small pharma and biotechs to: prioritize portfolios, identify knowledge gaps, prioritize and design experiments, predict optimal drug properties, and support clinical trials and indication/patient selection.

  • Senior Scientist, Biology

    We are currently seeking a talented and innovative Senior Scientist, Biology to join our team in Lincoln, MA, (the Boston/Cambridge area). The ideal candidate will work closely with Pharma and Biotech teams to provide biological expertise for the development of mathematical models that help drive decisions in drug development. Models include pathways (intra- and cell-cell signaling), disease (description and mechanistic), quantitative systems pharmacology, mechanistic PKPD, and traditional PKPD preclinical and clinical modeling of small and large molecules and novel therapeutics.