Shape Therapeutics is a biotechnology company developing breakthrough gene therapy technologies to treat the world’s most challenging diseases. ShapeTX gene therapy platform comprises RNAskip™, RNAfix™, and RNAswap™ payload technologies, next-generation tissue-specific AAVid™ delivery technology, and SquareBio, a solution to scalable gene therapy manufacturing based on industrialization of human stable cell lines. At the core of these technologies reside ShapeTX AI analytic platform, where data drives decisions in building technology today to enable gene therapies of tomorrow. ShapeTX is committed to data-driven scientific advancement, passionate people, and a mission of providing life-long cures to patients. Shape Life!
At ShapeTX, we are a dynamic team of professionals who are dedicated and passionate about making cures a reality. Through diversity of thought, scientific knowledge, professional rigor and focus we are merging cutting-edge science with extensive drug development expertise to unlock cures to many debilitating diseases.
Shape Therapeutics is headquartered in Seattle, Washington with a satellite site in Cambridge, Massachusetts.
ShapeTX is looking for a highly motivated and collaborative individual to join the fast-growing Analytics and Informatics team as a Scientist, Computational Biology. In this role, the successful candidate will integrate diverse databases and multi-omic datasets to analyze a range of biological questions centered around treating genetic diseases. The ideal candidate will have a strong background in human genetics and analyzing high dimensional datasets with a demonstrated record of scientific accomplishment. This individual will have the unique opportunity to develop end-to-end analytical pipelines that help advance novel gene therapies that treat serious human genetic diseases.
Roles and Responsibilities:
- Design and implement novel strategies to analyze high dimensional biological data (e.g. NGS, transcriptomic, proteomic, single cell) focused on better understanding and treating human genetic diseases.
- Integrate genomic databases and external data with internal data to develop testable hypotheses regarding optimal targets (e.g. differential expression, network inference) and interventional strategies (e.g. A→G correction, nonsense read-through, exon-skipping, gene replacement).
- Interface with experimentalists to facilitate rigorous experimental design and efficient decision-making.
- Utilize and cross-compare diverse analytical tools and strategies to maximally leverage disparate internal and external data types.
- Author and maintain clearly documented code, and effectively collaborate and iterate for further optimization and extension.
Qualifications and Requirements:
- Ph.D. in Computational Biology, Genomics/Genetics, Bioinformatics, or a related discipline.
- Ability to work both independently and collaboratively with a team-player mindset.
- Ability to tackle challenges with a problem-solving, “can-do” attitude, and a desire to work in a fast-paced, start-up environment.
Skills and Experience:
- Proven track record of integrating internal and external multi-omic data and databases to develop analytical pipelines that address biological hypotheses of genetic diseases.
- Takes initiative to identify and develop innovative computational methodologies to address these challenges.
- Domain expertise in human genetics and its relationship to disease; familiarity using databases of human genetic variation (e.g. dbSNP, gnomAD, OMIM, ClinVar) and understanding of concepts underlying variant effect interpretation (e.g. CADD).
- Demonstrated proficiency in one or more object-oriented programming languages (Python, Bash, R, Julia).
- Experience using cloud-based computing services (e.g. AWS).
- Committed to coding best practices using version control systems (e.g. Git).
- Capable of simultaneously progressing multiple projects involving different data types and structures.
- Superb attention to detail, meticulous organizational skills, and effective and proactive communication.
- Experience with statistical modeling and inference using biological datasets is preferred.
- Experience in molecular biology, NGS or proteomic experimental protocols, or high-throughput screening is a plus.
- Knowledge in protein biology, RNA biology, or neurobiology also is a plus.
If the notion of integrating human genomics with novel NGS and molecular datasets towards providing an end-to-end gene therapy solution for millions suffering worldwide from rare genetic disorders motivates you like it does us, we’re very excited to have you join us!