Bioinformatics Analyst I

Job Detail

KEY ROLES/RESPONSIBILITIES

Work closely and learn from expert principal investigators within LTG while supporting a broad portfolio of projects.
Review, QC, and integrate data from multiple sources (multi-omics studies).
Develop and document the pipelines designed for High Performance clusters and to be scalable.
Maintain and analyze genomic datasets related to Hi-Chip, sc-RNA-seq, long-read sequencing, etc.
Sequence Read Archive (SRA) database retrieval and deposition, mining of public databases, such as TCGA, GTEx and other resources.
Assist with data analysis, organize results into clear presentations and concise summaries of work, in formats useful for scientific interpretation.
Document all analyses and pipelines used in support of reproducible and FAIR research.
Work closely with LTG PIs in support of scientific manuscript development, submission, revision activities with significant co-authorship opportunities.

BASIC QUALIFICATIONS

To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:

Possession of a bachelor’s or master’s degree from an accredited college/university in genetics, genomics, bioinformatics, biostatistics, computer science, computational biology or another related field, according to the Council for Higher Education Accreditation (CHEA) or four (4) years relevant experience in lieu of degree. Foreign degrees must be evaluated for U.S equivalency.
The ability to construct practical computational pipelines for data parsing, quality control and analysis for large-scale genetic or genomics datasets.
Strong programming skills (e.g., in R, Python).
Demonstrable shell scripting skills (e.g., bash, awk, sed).
Experience working in a Linux environment (especially a HPC environment or cloud).
Ability to obtain and maintain a security clearance.

PREFERRED QUALIFICATIONS

Candidates with these desired skills will be given preferential consideration:

Experience with processing and analyzing large datasets for at least one of the following: GWAS, whole genome/exome sequencing, transcriptomics or other popular next-generation sequencing applications.
Proficiency with core statistical and bioinformatics methods (linear regression, logistic regression, eQTL analysis, LDscore regression, credible set and colocalization analysis, etc.).
Familiarity with public genomic tools, databases, and utilities (UCSC Genome Browser, TCGA, ENCODE, 1000 Genomes, dbGAP, GTEX, SRA NCBI, etc.).
Experience with various environment/dependency management tools (e.g. pip, venv, conda, renv).
Knowledge of DevOps tools and technologies, such as Docker/Singularity, GitHub for code management.
Team-oriented with demonstrated ability to self-educate in current bioinformatics techniques and resources.
Ability to multi-task in a fast-paced environment, organize and execute multiple projects in parallel both independently and as part of working groups.

Application ends in 5d 1h 33min