Computational Scientist I - McCarroll Lab

The Broad Institute•Cambridge, MA

5d•Hybrid

About The Position

The Broad Institute of MIT & Harvard is looking for an outstanding Computational Scientist to join the McCarroll Lab (http://mccarrolllab.org/). The goal of the McCarroll lab is to understand how genes and alleles give rise to human neurobiological variation and to the brain’s biological vulnerabilities to DNA-repeat disorders and major mental illness. The successful candidate will join a multidisciplinary group and support a wide variety of projects conducting cutting-edge research on the molecular, cellular, and neurobiological mechanisms of brain disorders. This position will work in close collaboration with scientists, project and data managers, and software engineers on a variety of projects spanning between the Broad Institute and Harvard Medical School. The Computational Scientist will be actively engaged with multiple projects, and will need to have exceptionally strong computational and organizational skills as well as the ability to prioritize tasks appropriately.

Requirements

PhD in Computer Science, Mathematics, Statistics, Biology, Bioinformatics, or a related field
Minimum years of experience: 2+ years of direct work experience
Fluency in Python and/or R
Solid understanding of one or more related areas of biology
Experience working on multiple complex projects simultaneously
Excellent organizational skills and ability to prioritize tasks thoughtfully
Demonstrated ability to interact with a diverse and interdisciplinary group of both professional staff and academic trainees; including project managers, data analysts, software engineers, and laboratory scientists
Experience analyzing next-gen sequencing data, preferably single-cell RNA-seq data
Knowledge of data science principles (e.g., statistics, algorithms)

Responsibilities

Develop, refine, optimize and apply analysis methods to a variety of datasets, including single-cell RNA-seq, ATAC-seq, and multiome data.
Analyze data to address biological questions about disease pathogenesis, genetics, and biomarkers.
Create innovative new analysis methods allowing the project team and the broader scientific community to access, analyze and interact with data generated by projects; carefully document and annotate source code.
Develop data-visualization capabilities for the research team, and create visualizations of data for a variety of internal and external stakeholders.
Work with scientists, data managers and software engineers toward the creation of optimal analysis workflows for high-data-volume experiments.
Participate in project-planning and project-analysis meetings as well as software-planning meetings, communicating the needs and tradeoffs.