Research Data Sciences Co Op

Role Summary:

The Data Sciences team at Dyne Therapeutics is seeking a Co-Op student to participate in research on innovative therapeutics for rare neuromuscular disease. You will apply your computational skills to the engineering and analysis of data in our drug discovery programs. You will apply your skills in machine learning and data engineering on complex multi-omics and preclinical datasets. In partnership with biology and bioinformatics scientists, you will contribute to insights about novel therapeutic mechanisms and their impact on disease. The successful candidate will be collaborative, detail-oriented, enjoy learning new skills and working in a fast-paced environment.

Dyne’s Co-Op Program offers students the opportunity to learn and work at a cutting-edge company in the biotech industry. Through the course of the co-op, you will gain a broad understanding of a variety of experimental and bioinformatics research methods in therapeutic discovery and development.

This role is for a period of 6 months, beginning in July.

This role is based in Waltham, MA without the possibility of being a remote role. Applicants must be able to relocate to the area. 

Primary Responsibilities Include:

• Implementation of computational methods and data engineering for omics data sets such as Next Generation Sequencing (NGS). 
• Evaluation of computational methods for classification of therapeutic R&D data for understanding of patient population and disease biology. 
• Management, integration, and visualization of preclinical and translational data.
• Analytical method development with regular communication of progress to the project team and accurate documentation of technical work.
• Presentation of learnings and findings at the end of the Co-Op term

Education and Skills Requirements:

• Undergraduate student in a relevant field, such as bioinformatics, cheminformatics, applied mathematics, or computer science or equivalent experience 
• Programming experience in R and/or Python; familiarity with code management and notebook IDE such as jupyter 
• Experience with statistical and machine learning methods, such as supervised and unsupervised classification
• Interest in working with biological data types, such as biomolecular sequence, transcriptomics and proteomics
• Understanding of relational databases, SQL, json, or other data management structures
• Strong organizational skills and solid written and oral communication 

#LI-Onsite

Tags