Pioneering Medicines: Bioinformatics Co-Op

Flagship Pioneering•Cambridge, MA

4d•Onsite

About The Position

This position is open exclusively to current Northeastern University Co-Op students. Work Period: January 2027 to June 2027. This is a full-time, 40 hours per week position with 5 days in office. As a Bioinformatics Co-op, you will work alongside computational and bench scientists to analyze large-scale biological datasets supporting active drug discovery programs. You'll gain hands-on experience with single-nucleus (snRNA-seq) analysis and disease genomics, while learning core bioinformatic processes and building skills spanning genomics, data science, and pipeline development across multiple therapeutic programs. The co-op will lead a project analyzing and understanding quality control (QC) for single-nucleus RNA-seq (snRNA-seq) data, defining and applying QC metrics and filtering criteria to ensure data integrity ahead of downstream analysis. This project is embedded within an active Pioneering Medicines therapeutic program and will also serve as a capability build for the company, establishing reusable snRNA-seq QC standards and workflows for future programs.

Requirements

Currently enrolled in an undergraduate or Master's program in bioinformatics, computational biology, computer science, statistics, genetics, or a related field.
Proficient in Python or R, with working knowledge of Unix/Linux and version control (Git).
Strong problem-solving skills, curiosity, and the ability to work independently and in a team.
Clear written and verbal communication skills.

Nice To Haves

Familiarity with genomics or biological data types (NGS, single-cell, proteomics) preferred.
Exposure to statistics, machine learning, or workflow tools (Nextflow, Snakemake, Docker) is a plus.

Responsibilities

Analyze single-nucleus (snRNA-seq) and single-cell datasets to characterize cell populations relevant to disease biology.
Support disease genomics analyses, including variant interpretation and genotype-phenotype exploration, to inform target identification and validation.
Build, adapt, and run computational pipelines (e.g., RNA-seq, single-nucleus/single-cell, WGS/WES) using tools such as Nextflow, Snakemake, or custom scripts.
Apply statistical and machine learning methods to identify patterns in multi-omic and experimental data.
Query and integrate data from internal and public databases (e.g., TCGA, GTEx, CCLE, GEO).
Create clear visualizations and summaries to communicate findings to interdisciplinary project teams.
Document methods and code to ensure reproducibility and support downstream use by the team, while learning core bioinformatic processes and best practices from the broader team.