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 individual with strong interest and experience in data science and Next-Generation Sequencing (NGS) to join the fast-growing Analytics and Informatics team as a Data Analyst, Computational Biology. In this role, the successful candidate will process and analyze diverse molecular and NGS datasets covering a range of biological questions and requiring distinct analytical approaches. The ideal candidate will have experience applying statistical methods to high dimensional datasets for testing scientific hypotheses, as well as general familiarity with NGS components and data structures. This individual will have the unique opportunity to develop statistical and machine learning skills while helping advance novel gene therapies that treat serious human genetic diseases.

Roles and Responsibilities:

  • Design and implement strategies to reproducibly process NGS data to enable statistical and machine learning (ML) analyses that rigorously test biological hypotheses.
  • Interface with experimentalists to understand data structure and incorporate appropriate methodologies to ensure accuracy and facilitate decision-making.
  • Utilize and cross-compare diverse analytical strategies using quality control metrics to troubleshoot and optimize computational workflows for disparate data types.
  • Learn, test, and adapt new software and tools to streamline and improve data analysis and develop analytical skills (e.g. statistics, ML).
  • Write and maintain clearly documented code, and effectively collaborate and iterate for further optimization.
  • Coordinate efforts with the Analytics and Informatics team and adapt priorities based on dynamic business needs. 

Qualifications and Requirements:

  • Bachelor’s or Master’s degree in Computational Biology, Statistics, Genomics, Bioinformatics, Data Science, or a related discipline.
  • Ability to work both independently and collaboratively in a team-science environment.
  • Ability to tackle challenges with a problem-solving, can-do attitude, with a desire to work in a fast-paced, start-up environment.

 Skills and Experience:

  • Passionate about building tools and executing strategies that optimally leverage novel NGS data (e.g. small RNA-Seq, Ribo-Seq) to address biological hypotheses using statistical techniques.
  • Takes initiative to identify and troubleshoot innovative methodologies for streamlining computational workflows.
  • Demonstrated proficiency in a programming language (Python, Bash, R) and common data science tools (data.table, tidyverse, pandas).
  • Experience with cloud-based services (e.g. AWS) and version control systems (e.g. Git).
  • Capable of simultaneously progressing multiple projects involving different data types and structures.
  • Superb attention to detail and meticulous organizational skills.
  • Effective and proactive communication.
  • Experience with statistical modeling and inference using biological datasets is preferred.
  • Experience in molecular biology, NGS experimental protocols, or high-throughput screening is a plus.
  • Knowledge in protein biology, RNA biology, neurobiology, or genetic diseases is a plus.
  • Collaborative spirit with a team-player mindset.

If the notion of analyzing 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!

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