Bioinformatician I

The Jackson Laboratory•Farmington, CT

7d•$65,018 - $108,857

About The Position

The Bioinformatician will develop algorithms and computational resources to analyze how dysregulated RNA splicing contributes to human disease. Working with the PI and experimental collaborators, the role translates biomedical questions into algorithmic approaches, implements them as tested and reproducible software, and applies them to bulk, single-cell, and spatial transcriptomic data. The Bioinformatician takes scientific ownership of method development, advancing novel approaches through benchmarking, peer-reviewed publication, and open release. The work includes mechanistic interpretation of results by linking splicing changes to regulators and functional consequences, and proposing and prioritizing experimental validation. The position presents findings internally and externally, works independently with limited supervision, collaborates with lab members, and helps update and maintain widely used pipelines and tools. The role develops and applies methods for splicing analysis across patient-derived, model-system, and public datasets; integrates genomic variation and other molecular modalities; creates version-controlled, containerized, documented Nextflow pipelines; and contributes to manuscripts and grant applications. Minimum education is a bachelor's degree, with at least two years of experience stated in the minimum requirements. A PhD is preferred. The posting also lists alternative qualifications of a bachelor's degree in bioinformatics or a relevant biological science plus five years of professional bioinformatics experience, or a master's degree in bioinformatics or a relevant biological science.

Requirements

Bachelor's degree is the minimum education requirement.
Minimum experience requirement: 2 years.
Alternative listed qualification: bachelor's degree in bioinformatics or a relevant biological sciences field plus five years of professional bioinformatics work experience, or a master's degree in bioinformatics or a relevant biological sciences field.
Deep knowledge of RNA biology and genomics, including splicing mechanisms, cis-regulatory elements, and splicing factors.
Demonstrated experience analyzing splicing in transcriptomic data, including knowledge of tool limitations and failure modes.
Proficiency in Python, R, and Bash, and experience with Nextflow workflow management.
Experience developing algorithms in machine learning, statistics, or genomics, demonstrated through novel methods or substantial extensions of existing methods.
Software engineering practices including version control, automated testing, containerization, and documentation.
Ability to read, critique, and apply primary literature relevant to projects.
Ability to lead projects and work independently with limited supervision.
Clear written and oral communication of methods and results to scientific audiences.
First-author publication in a peer-reviewed journal.

Nice To Haves

PhD in bioinformatics, biology, or a related field.
Experience with single-cell and/or spatial transcriptomics.
Experience with HPC or cloud compute environments at scale.
Experience mentoring students or junior colleagues.
Experience with human cohort or controlled-access data, including TCGA, GTEx, or dbGaP, and splicing QTL analysis.
Experience with deep-learning models of sequence-to-splicing relationships.
Experience with long-read RNA sequencing and isoform-level analysis.
Prior projects on splicing in a disease context, including identification of disease-associated splicing events or splicing-factor dysregulation.

Responsibilities

Design, implement, test, and benchmark algorithms and statistical or machine-learning models for splicing analysis, including event detection and quantification, differential splicing, isoform-level analysis, and prediction of variant effects on splicing.
Lead method development through the manuscript-ready stage and serve as a co-author on resulting publications.
Apply methods to patient-derived, model-system, and public bulk and single-cell datasets to identify disease-associated splicing alterations, upstream drivers, and downstream consequences.
Integrate splicing data with genomic variation, long-read transcriptomics, epigenomic, proteomic, and other modalities to support mechanistic interpretation.
Build version-controlled, containerized, documented Nextflow pipelines and openly release tools to support reproducible analyses.
Work with the PI and experimental collaborators to define research questions, design validation experiments, and interpret results.
Present pipelines, methods, and results at internal meetings and external conferences; contribute to manuscripts and grant applications.
Keep current with literature in splicing, RNA biology, and computational genomics.
Mentor junior staff and trainees as needed.
Assist and collaborate with other lab members to advance their research and help update and maintain widely used pipelines and tools.