What if…you could join an organization that creates, resources, and builds life sciences companies that invent breakthrough technologies in order to transform health care and sustainability?
Radial Therapeutics, Inc. is a privately held, well-funded, early-stage biotechnology company leveraging novel insights in RNA biology. Radial Therapeutics’s proprietary platform identifies novel disease targets and develops new therapeutic modalities targeting RNA, providing new strategies to treat or cure a range of diseases including rare genetic diseases, neurological diseases, and cancers.
We are seeking a Scientist I in Computational Biology to join a multidisciplinary team dedicated to finding novel avenues to drug RNAs using small molecules. Applying rigorous, quantitative and statistical methods, you will pioneer the development of sophisticated computational models to answer key biological questions for uncovering novel RNA-small molecule interactions. In particular, your advanced knowledge of comparative genomics and computational modeling together with broad expertise across RNA biology, data engineering and bioinformatics data processing will allow us to crack the code to drug RNAs with small molecules. Operationally, this position will require close collaboration with experimental biologists and AI scientists to generate and test hypotheses in the field of RNA-small molecule interactions.
- Translate biological questions into computational problems
- Use publicly available data, proprietary data, comparative sequence analysis and other evolutionary information to build computational models aiming to uncover novel RNA-small molecule interactions
- Actively participate in designing experimental approaches to generate relevant data as well as to validate computational insights
- Develop computational frameworks to systematically characterize results from machine learning models from a biological perspective and provide feedback on improving the models
- Contribute new ideas, strategies and approaches that further the company’s goals
- Create concise data visualizations and communicate complex results to an interdisciplinary audience
- PhD in computational sciences (e.g. computational biology, computer science or mathematics) with applications in biology, or PhD in biological sciences with strong computational expertise
- 1+ years industry or postdoctoral experience is preferred
- 5+ years experience in processing, analyzing and modeling large biological datasets (e.g. genomic, transcriptomic) to answer key biological questions
- Deep expertise in comparative genomics and comparative sequence analysis, including whole genome alignments (e.g. Cactus), synteny analysis (e.g. SynChro), annotation (e.g. InterProScan, KAAS) and phylogenetics, both using maximum likelihood (e.g. SeaView) and Bayesian models (e.g. MrBayes)
- Hands-on experience with a variety of DNA/RNA/protein tools for sequence searches and consensus determination, including alignment-based (e.g. BLAST, MUSCLE, Mafft) and Markov/covariance-based (e.g. nhmmer, Infernal, CMfinder)
- Experience predicting and analyzing 2D RNA structures, including using chemical probing data (e.g. DMSMaPseq, SHAPEseq) to inform RNA structures
- Advanced knowledge of statistics (frequentist and Bayesian) for analyzing complex, multidimensional data
- Strong programming skills in Python and scientific libraries such as pandas, numpy, scipy, matplotlib, biopython. Proficiency in other programming languages such as Perl, C/C++ and java is a plus.
- Experience developing data processing, engineering and statistical analysis workflows using AWS services such as S3, EC2, ECS, AWS Batch, NextFlow
- Foundational knowledge of machine learning and small molecule therapeutics is a plus
Location: Cambridge, MA
More About Flagship Pioneering
Flagship Pioneering conceives, creates, resources, and develops first-in-category life science platform companies to transform human health and sustainability. Since its launch in 2000, the firm has, through its Flagship Labs unit, applied its unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in over $100 billion in aggregate value. To date, Flagship has deployed over $3.1 billion in capital toward the founding and growth of its pioneering companies alongside more than $19 billion of follow-on investments from other institutions. The current Flagship ecosystem comprises transformative companies, including Moderna (NASDAQ: MRNA), Sana Biotechnology (NASDAQ: SANA), Seres Therapeutics (NASDAQ: MCRB), Axcella Health (NASDAQ: AXLA), Denali Therapeutics (NASDAQ: DNLI), Foghorn Therapeutics (NASDAQ: FHTX), Indigo Ag, Generate Biomedicines, Tessera Tx, and others.
Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We believe pioneering is best done by teams, and that it is a process that can be taught, learned, and replicated. Learn more about our Company Creation Model.
Can Breakthrough Innovations Be Made Systematically? A Conversation With Noubar Afeyan, Flagship Pioneering’s CEO.
Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.