PatientsLikeMe: Director, Bioinformatics
160 Second Street
Cambridge, MA 02142


PatientsLikeMe is a patient network that improves lives and a real-time research platform that advances medicine. Through the network, patients connect with others who have the same disease or condition and track and share their own experiences. In the process, they generate data about the real-world nature of disease that help researchers, pharmaceutical companies, regulators, providers, and non-profits develop more effective products, services and care. With more than 500,000 members, PatientsLikeMe is a trusted source for real-world disease information and a clinically robust resource that has published more than 80 peer-reviewed research studies.

We are in the extraordinary position of building a massive health data set, as we are adding longitudinal multiple-modality omics data to our unique patient-reported health data. We are looking for a highly motivated Bioinformatics Scientist to join the Translational Sciences department at PatientsLikeMe. The successful candidate will have the opportunity to impart a significant impact on the development of this program. The ideal candidate will be eager to identify, design, implement and document effective solutions. The candidate must be comfortable working as part of a team, often within areas outside of their expertise, and requiring effective communication with colleagues of various backgrounds, skill sets, and educational experience. The multi-disciplinary nature of the project affords great opportunity for a candidates growth and development.


  • Set strategy for the Bioinformatics team
  • Recruit, Hire and Develop and Lead a team of high performing scientists.
  • Build and maintain robust, scalable pipelines for the QA/QC, processing, and analysis of omics data including, but not limited to, WGS, RNA Seq, proteomics, metabolomics, and DNA methylomics
  • Help evaluate the best parameters for biological and clinical analyses
  • Identify strategies and algorithms to most effectively use multi-platform, multi-batch data
  • Communicate results in a clear, concise, and effective manner using various statistical metrics and visualizations
  • Work closely with multiple internal teams including data engineering, clinical operations, knowledge management, translational, and computational biologists, as well as externally with omics measurement vendors


The ideal candidate will have many of these qualifications:

  • Education: Masters or PhD in a related field (e.g., bioinformatics, mathematics, statistics, bioengineering, computer science) and experience with the skills mentioned above, or Bachelors with commensurate experience.
  • 5+ years of professional experience.
  • Ability to set strategy and lead teams.
  • Experience developing and maintaining -omics pipelines in a variety of languages
  • Familiarity with statistical analysis of complex data, preferably in the biological sciences
  • Proficiency in at least one general-purpose programming language (Python preferred). Some R/MATLAB or similar experience strongly desired. Must be comfortable working in languages that are not your primary.
  • General understanding of machine learning concepts
  • Some familiarity with biology/genomics/systems biology/immunology
  • Experience working with databases of various types (postgres, mysql, mongo, etc), and awareness of data warehouses, data lakes, and other Big Data concepts
  • Significant experience wrangling data
  • Know how to interface with [restful/graphql] APIs
  • Have used AWS/Google Cloud/Azure to perform project-critical tasks
  • Have a heartfelt appreciation of science and a respect for the use of data to identify the closest approximation of truth Have the cognitive dissonance of a desire to get the most correct answer possible while acknowledging the business need for rapid iteration and the inherent imperfection that requires
  • Have empathy for consumers of information which generates a desire to produce effective displays/visualizations/presentations of complicated data