Applied BioMath is revolutionizing drug invention by helping partners accelerate best-in-class therapeutics into the clinic. We are currently seeking a talented and creative “Senior/Principal Scientist, Biology” to join our rapidly expanding team either in Concord, MA, (the Boston/Cambridge area) or Pleasanton, CA (the San Francisco Bay area). 

Our team members are innovators and entrepreneurs at heart who are passionate about pioneering a paradigm shift in drug discovery: that with thoughtful use of quantitative models, non-intuitive threats to therapeutic programs can be anticipated and their lessons applied to future drug design (e.g., Is higher affinity binding always better? Does targeting ligands unintentionally increase their levels?). Our scientists collaborate across disciplines to integrate rigorous mathematics with disease & drug mechanisms to build predictive systems pharmacology models to accelerate and de-risk drug development timelines. 

The Biology Department's role at Applied BioMath is central to our cross-functional teams. In the Biologist role, you will work closely on projects with internal and external client teams to provide biological expertise and strategic guidance for the development of mathematical models: 

  • How can a qualitative understanding of a biological process be represented quantitatively? 
  • What assumptions are appropriate to enable modeling? 
  • How do the literature and client generated data relate to each other? 
  • How can a critical parameter be inferred if it was not measured directly? 
  • How best to interpret modeling results for the most sound conclusions?

The ideal candidate will take initiative to create new science that helps drive drug development decisions. In addition, the ideal candidate loves to learn, lead, and challenge yourself and others.  


  • Provide biological expertise for ABM-internal and customer projects.
  • Demonstrate clear understanding of client's goals and work effectively toward achieving them
  • Ability to communicate simulation results in the context of drug development and discovery
  • Review published and ABM customer data to understand disease biology, drug properties, mechanism of action, pharmacology
  • Assess technical quality and utility of client and literature derived data to support the modeling strategy
  • Generate project bibliographies and parameter tables in collaboration with modelers and other team members
  • In a dry-lab setting, stay abreast of key developments in disease biology, molecular/cell biology techniques and in drug discovery and development


  • PhD or MS in biology, immunology, systems biology, biochemistry or related field
  • Ability to think critically about model inputs and outputs
  • Ability to approach biological problems from a quantitative perspective
  • Ability to quickly learn new areas of biology through reading the literature
  • Strong interpersonal and communication skills
  • Experience working in multi-disciplinary teams
  • Biotech/pharma industry experience
  • At least 3 years of industry laboratory experience with biophysical, biochemical, molecular and cell biological methods
  • At least 3 years of drug discovery and development experience
  • Knowledge of preclinical or clinical pharmacology
  • Experience with mathematical modeling or systems biology is a plus
  • Record of scientific achievement through peer-reviewed publications, invited oral presentations, or patents

Work Locations:

  • Strong “remote work” culture (new employees can be onboarded virtually)
  • Concord, MA (headquarters)
  • Cambridge, MA (satellite office) 
  • Pleasanton, CA (West coast office)

Applied BioMath ( is revolutionizing drug invention by helping partners accelerate best in class therapeutics into the clinic. We do this by integrating disease biology, therapeutic mechanism of action, rigorous mathematics, high performance computing mathematical modeling 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, support clinical trials, enable indication/patient selection, and help understand deep biology or generate testable hypotheses. Our results have reduced costs and accelerated timelines. Our approaches have been proven across multiple therapeutic areas including oncology, immunology, immuno-oncology, cardiovascular, CNS, for a multitude of indications. We work closely with drug program teams, scientists, program managers, protein engineers, and chemists, as well as senior managers, to improve the outcome of drug programs in research, development, and clinical trials. We are frequently thought of as a member of the project team.  If interested, please contact