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 biostatistics to join the fast-growing Analytics and Informatics team as a Data Scientist, Biostatistics. In this role, the successful candidate will develop and apply novel statistical modeling techniques across diverse molecular, preclinical and clinical datasets to further revolutionize our gene editing, delivery, expression, and manufacturing methods under development. The ideal candidate will have extensive experience developing and applying unsupervised, semi-supervised, and supervised statistical modeling approaches to biological datasets. This individual will have the unique opportunity to throw themselves headlong into refining the statistical techniques we use to advance novel gene technologies and therapies for treating serious human genetic diseases.
Roles and Responsibilities:
- Design and implement statistical inference frameworks to model preclinical and clinical data involving RNA editing, payload delivery, and complex disease biomarkers.
- Interface with experimentalists to enable rigorous experiment designs that are sufficiently powered to test hypotheses of interest.
- Identify suitable analytical strategies for disparate data types with deep understandings of available statistical inference methods, their assumptions and data properties.
- Communicate analysis outcomes efficiently with leadership and stakeholders to facilitate data-driven decision making.
- Author and maintain clearly documented code, and effectively collaborate and iterate for further optimization.
Qualifications and Requirements:
- Doctorate, or Master’s degree with over six years of experience, Statistics, Biostatistics, Computer Science, 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 solving complex analytical problems related to preclinical and clinical data, including how best to analyze new data types generated from novel sequencing and molecular technologies.
- Takes initiative to identify, develop, and optimize innovative statistical learning approaches for predictive models of gene editing outcomes.
- Demonstrated proficiency in a programming language suitable for statistical analysis (Python, R, SAS, Julia) and common data science tools (pandas, tidyverse).
- Deep understanding in frequentist and bayesian statistical inference frameworks.
- Experience with statistical modeling and inference using biological, preclinical or clinical datasets.
- Experience with cloud-based services (e.g. AWS) and version control systems (e.g. Git).
- Capacity to simultaneously progress multiple projects involving different data types and structures.
- Intellectual curiosity, superb attention to detail, meticulous organizational skills, consistent follow-through, effective and proactive communication, and collaborative spirit with a team-player mindset.
- Experience in working with clinical datasets under a regulatory setting is a plus; as is knowledge of protein biology, RNA biology, neurobiology, or genetic diseases.
If the notion of analyzing novel molecular, preclinical or clinical 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!