PatientsLikeMe is a real-time research platform and the world’s largest personalized health network, working to bring the patient voice to research, development and public policy. With over half a million members, PatientsLikeMe is a trusted source for real-world disease information and its patient-generated data form the basis of more than 100 peer-reviewed scientific studies. PatientsLikeMe is now a central part of iCarbonX's Digital Life Alliance, whose partner organizations work in collaboration to reach a deeper understanding of the medical, behavioral and environmental factors that can accelerate disease or optimize health.
Role and Responsibilities
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, to discover biomedical insights and ultimately help patients navigate their path to a healthier state. Therefore we are looking for a highly motivated computational biologist to join the Biology and Bioinformatics group at Patients Like Me. The team is heavily involved in laying the foundation of this project and a successful candidate will have the opportunity to have a strong impact on the development of this program. The multi-disciplinary nature of the project and extensive systems biology experience of the group in academia and pharma also affords great opportunity for a candidate’s growth and development.
Under supervision of the Computational Biology Lead, the Research Associate will establish multi-omics analysis pipelines, including methods for handling and integrating NGS, metabolomics, proteomics, and DNA methylomics data, in the cloud using open source or proprietary software and help evaluate the best parameters for biological and clinical analyses. He/She will perform statistical analyses and summarizations of the data, and communicate their results in a concise and clear manner with statistical metrics and visualizations. He/She will work closely with the the system biologists to package the analyses into tools that the biologists can re-use for the interpretation of the omics data.
- Master or B.Sc in a related field (e.g., mathematics, statistics, biology, bioengineering, computer science) or experience with the aforementioned skill sets
- Proficiency in R and Bioconductor or Python
- Linear modeling and statistical testing in R
- Machine learning notions (cross-validation)
- Gene ontology enrichments or gene set enrichment analysis;
- Publicly available databases (e.g., GEO, MSigDB, String)
- Command line tools for NGS (e.g.,BWA, STAR)
- Linux command line and preferably Cloud experience (e.g., AWS)
- Clustering methods
- Visualization development tools (e.g., gplots, ggplot2, shiny)
- Good communication skills and some knowledge of biology are necessary for efficient interaction with the rest of the group
- The ability to be creative and the motivation to pursue research questions independently, as well as ability to quickly refine your statistical models in collaboration with the system biologists
- Needs to be comfortable working as part of a team in a highly dynamic environment
- Experience with mass spectrometry data is a plus
- Must be local to our Cambridge office or willing to relocate