Bioinformatics Co-op (Spring 2026)

Role Summary

Bioinformatics Co-op (Spring 2026) at Wave Life Sciences. Support bioinformatics, computational biology, and human genetics efforts in drug discovery programs; design and execute sequence-based machine learning projects and develop tools for oligonucleotide therapeutics.

Responsibilities

• Lead and oversee large data projects; design and build pipelines that process and analyze related data, store summary results and integrate with the corporate ELN
• Build applications to support bioinformatics and human genetics pipelines in R (shiny) and Python (streamlit)
• Develop, deploy, manage, and run applications using Docker containers
• Contribute to best practices for Wave’s storage-and-compute scalability of large bioinformatics datasets
• Develop pipelines that determine mapping coordinates and homology of oligonucleotides to genomes and transcriptomes
• Espouse best practices for version control for developed software (Git)

Qualifications

• Experience with common human genetics resources and techniques
• Deployment of interactive applications from scripting languages like R and Python (Shiny and streamlit)
• Experience with relational databases; hands-on experience with MySQL is a plus
• Experience in common NGS analyses such as mapping reads, quantification of gene expression, and/or variant calling
• Experience with common bioinformatics tools like Samtools, GATK, Bowtie, HISAT2, BLAST, htseq-count, DESeq, Salmon, STAR, or Subread
• Knowledge of Illumina sequencing data analysis; PacBio and/or Nanopore sequencing is a plus
• Familiarity with UNIX computing environment and proficiency in either Python or R scripting; experience with Docker and Nextflow pipeline is strongly recommended
• Ability to communicate findings of NGS data to a general scientific audience in both oral and written forms

Skills

• Strong coding and software development background; willingness to work in an interdisciplinary team
• Proficiency in Python or R; experience with Docker and containerized workflows
• Experience with high-performance compute environments

Education

• Not specified

Additional Requirements

• Note: No explicit travel or physical demands were listed as essential in the provided description.