PatientsLikeMe has over a decade of experience operating a unique platform where patients with over 2500+ conditions can come together and share data: clinical data (e.g., what treatments they are taking or what their diagnoses are), experiential data (e.g., how do you rate how you feel today?), and patient reported outcomes (e.g., the ALSFRS, which is a functional rating scale that ALS patients can take in order to track their disease progression). These data can help individuals track the details of their condition such that they can observe patterns and make improvements.
Now, PatientsLikeMe has embarked upon DigitalMe, a program whereby we are not only capturing the data types outlined above, but also longitudinal blood samples from our patients that wish to participate. On these samples, we will have the ability to run a full omics panel: DNAseq, RNAseq, methylomics, proteomics, antibody immunosignatures, immunosequencing, metabolomics for now but we are continually evaluating the newest technologies in high-throughput biological measurements. For each patient sample, we can have millions of biological measurements, plus all of patient-generated health data, in order to do our research. The remit for our team is that we make sense of all of this, in order to help patients understand their own biology, how that biology relates to their experience and their clinical data, and how they can make modulations to their daily life to optimally thrive.
As Senior Scientist for Computational Biology, you will work within a creative team of dedicated scientists to build a technology stack capable of storing, processing and analyzing multi-omic measurements taken from longitudinal biosamples from thousands of individuals. You will participate in evaluating the strengths and weaknesses of multiple ‘omics platforms, and building the data analysis pipelines for those platforms in a scalable fashion. You will also aid in the development of creative and novel methodologies to integrate different kinds of high-throughput data modalities, together with clinical measures and patient reported outcomes into biological models for individual patients and groups of patients. You will build and analyze large-scale patient data sets. You must work closely with the systems biologists that will be evaluating the outcomes of your computational analyses in order to deliver tools for biological discovery.
You will also be responsible for working with the biological knowledge management team and engineers to implement the necessary infrastructure to support data storage, architecture, knowledge management, and tool development. This will include evaluating the technologies of our partners and making strategic decisions about how to either integrate this code base or develop new code base to support future operations. When necessary, you may be called upon to support scientific consulting engagements, and you will be expected to contribute to publications in peer-reviewed journals with significant impact. You will be responsible for supporting the development of strategic plans for the development and evolution of the technology stack. We are particularly interested in candidates that have experience with mass spectrometry.
Title is commensurate with experience. The ideal candidate possess a significant number of the qualifications/capabilities listed below:
- D in Computational Biology, Bioengineering, Bioinformatics, Statistics, Biostatistics, Data Science or a related discipline (or a B.S/M.S. with commensurate experience), plus at least 1-5 years of experience in industry
- Ability to communicate complex computational biology concepts to biologists and lay persons
- Freshman college level understanding of molecular biology.
- Ability thrive in a fast-paced, demanding, and growing environment
- Proven track record in establishing data analysis pipelines and building tools for other computational biologists and computationally savvy biologists
- Expertise in complex analytical techniques for omics data. Experience with integrating multi-omics data is a plus
- Solid working knowledge of machine-learning approaches and applications to patient data
- Ability to collaborate with AI and data science experts
- Desire to solve extremely complex problems with scientific rigor, integrity and systematic attention to detail
- Outstanding team player and collaborator. Ability to work independently to deadlines
- Experience with mass spectrometry, particularly for metabolomics, proteomics, or lipidomics is a plus
- Must be local to our Cambridge office, or willing to relocate