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Senior Scientist, Computational Biology

Cambridge, MA - Research and Preclinical Sciences Full-Time
Scholar Rock is a clinical-stage biopharmaceutical company creating a robust pipeline of therapeutics with the potential to transform the lives of patients suffering from serious diseases, including neuromuscular disorders, cancer, fibrosis, and anemia. Our proprietary platform is rooted in our structural biology insights into the activation of latent growth factor precursors and drives the discovery and development of monoclonal antibodies that locally and selectively target these signaling proteins at the cellular level.  By exploiting structural differences in the precursor, or latent form of growth factors, Scholar Rock intends to avoid the historical dose-limiting safety challenges associated with inhibiting the mature form of growth factors for therapeutic effect.
Our lead product candidate, SRK-015, is a highly specific inhibitor of myostatin activation currently being evaluated in a Phase 2 proof-of-concept trial for the treatment of patients with Type 2 and Type 3 Spinal Muscular Atrophy (SMA). SRK-015 has received Orphan Drug Designation in both the US and EU.
Our second product candidate, SRK-181, is a potent and highly selective inhibitor of latent TGFβ1 activation.  A Phase 1 proof-of-concept trial will be initiated in Q1 2020 in patients with solid tumors. We believe SRK-181 has the potential to meaningfully increase the number of patients who can benefit from checkpoint inhibitor therapy by overcoming primary resistance to anti-PD-(L)1 antibodies.
Summary of Position:

We are seeking a highly motivated computational biologist to join us in developing best-in-class antibody-based therapeutics across multiple disease areas.  This position affords an opportunity to build and lead a robust data science platform, interfacing with research and clinical development teams and with external vendors. Consequently, he/she will significantly impact interdisciplinary programs across Scholar Rock’s R&D pipeline.  The successful candidate will effectively translate biological questions into data science approaches supporting clinical biomarker identification and validation; drug discovery, preclinical efficacy, and mechanism of action studies; and new target validation efforts.
Key Responsibilities
  • Using internal and publicly available datasets, work collaboratively within scientific and clinical projects to identify biomarkers for patient stratification and indication selection.
  • Support preclinical- and discovery-stage projects by translating scientific questions into hypotheses that can be addressed using computational workflows, data visualizations, and statistical testing.
  • Effectively communicate data analysis processes, results, and additional deliverables to internal project teams.
  • Qualifications:
  • Advanced degree (PhD or Masters) in computational biology/bioinformatics, or equivalent competency
  • Minimum of two years of experience in experimental design, data processing, statistical analysis, and data analysis/reporting using biological ‘omics datasets, particularly transcriptomics. 
  • Experience accessing and analyzing data from public domain transcriptomic, proteomic, and similar data sources.
  • Fluency with standard bioinformatics pipelines and packages such as Linux, R/Bioconductor, Python, Git, or related tools.
  • Experience designing, maintaining and optimizing cloud-based high-performance computing clusters and/or pipelines a plus
  • Knowledge of multiple disease areas is desirable, particularly experience with cancer biology, fibrosis, or muscle biology.   Hands-on wet lab experience is also a plus.
  • Must be independent, curious, team oriented, and able to thrive in an entrepreneurial and multidisciplinary environment.
  • Excellent communication and presentation skills, with an ability to effectively translate ideas between biology and data science. 

  • Scholar Rock is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees