Associate computational biologist in applied machine learning / data analytics
Organisation Name: Broad Institute
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Job Description:
The Vector Engineering laboratory led by Dr. Ben Deverman within the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and MIT aims to expand the impact of gene therapy by engineering next-generation adeno-associated virus (AAV) delivery vehicles. We seek to address the current limitations of AAV vectors by enhancing their ability to efficiently and specifically deliver genes to target organs and cell types, improving their manufacturing, and rendering them less susceptible to immune inactivation. Our laboratory uses high-throughput screening assays that leverage next generation sequencing (NGS) and custom DNA library synthesis, generating data that is quantitative and reproducible. These data are well suited for leveraging advances in machine learning to more rapidly achieve our goal of making effective gene therapies for a wide range of currently untreatable diseases. We are looking for an exceptional computational biologist with data analysis and machine learning experience to join our rapidly growing, data-driven research team. You will work at the cutting edge of data science, protein engineering, and bioinformatics to integrate, curate, and mine complex high-quality datasets to accelerate innovation and guide experimental design. This is a role for a creative and ambitious person who is interested in working collaboratively with a multidisciplinary and supportive team. You will benefit from being surrounded by the Broad’s highly diverse and passionate bench and computational scientists who are at the forefront of generating and applying genetic data for the understanding of disease and for the advancement of medicine. Your responsibilities include: Contribute to, propose and lead technology-oriented ML projects on relevant datasets Plan projects and effectively prioritize goals Develop, apply, document, and maintain computational tools, both for your projects and to support analysis by our multidisciplinary team Develop and embed automated processes for predictive model implementation, validation, and deployment Identify opportunities to improve the robustness and rigor in the analysis and acquisition of biological datasets Keep on top of relevant scientific literature to ensure the use of optimal methods and evaluate and implement emerging advances Attend and present results at team meetings, plan projects and experiments, and communicate results to collaborators Contribute to progress reports, publications, and presentations at scientific conferences Requirements Experience applying ML on large, high-dimensional datasets Non-trivial data analytics experience Strong background in statistics Basic understanding of molecular biology (DNA, RNA, and protein) Demonstrated programming and algorithm design skills; Python and data visualization skills are essential Good teamworking skills and a demonstrated ability to communicate effectively across disciplines to facilitate productive collaborations with wet-lab scientists Flexibility to lead projects, collaborate, and work as a team member in projects led by other scientists. Preferred but not required Experience working with genomic or NGS data Strong background in bioinformatics / biostatistics / protein engineering Familiarity with Google Cloud Platform and SQL #LI-DNP All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. EEO is The Law - click here for more information Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled
