We have an exciting opportunity to join our team as a Senior Research Scientist. In this role, the successful candidate is responsible for development of software for the analysis, visualization, and interpretation of clinical and research cancer genomic data.
Carries out computational scientific analyses on large cancer genomics datasets. The position may include supervising junior technical and academic computational genomics staff and working independently within the scientific framework of the PI's laboratory.
Job Responsibilities :
- Applies statistical and machine learning approaches to study correlations, uncover features, and develop classifiers across large cancer genomics datasets.
- Develops and maintains custom software for the analysis, visualization, and interpretation of cancer genomics data.
- May make significant contributions to scientific papers including first or co-first author. Is a resource / author / co-author for reports and presentations.
- Presents regularly to laboratory and NYU research community. May present research work at local, regional, national, or international conferences.
- Helps to organize large genomic datasets including managing laboratory storage footprint.
- Implements and maintains high quality and cutting-edge genomics data processing pipelines, helps to scale
- algorithms and analyses across high performance computing and cloud systems, including optimizing speed and memory.
- Oversees the maintenance of lab computational environment, including maintaining and introducing best practices in the laboratory for software development and data organization.
- Helps formulate, prioritize, and manage team tasks around key milestones and strategic goals related to analysis and software development.
- May provide trainees with technical guidance and direction in the operation of dry lab best practices including maintaining laboratory documentation.
- May contribute to lab compliance and quality control.
Minimum Qualifications :
To qualify you must have a PhD and 3 years of experience
Deep familiarity with basic genomics tools (eg IGV, bwa, samtools) and file formats (eg .bam, .vcf, .bed, .bedpe),
facility with statistical hypothesis testing (eg generalized linear models, mixed models), basic machine learning
toolkit (eg unsupervised clustering, mixture models, random forests), high-performance computing (eg SLURM)
Preferred Qualifications :
Postdoctoral training or equivalent industry experience.
Established a record of publications, including first or co-first author contributions. Track record of open-source
software development on GitHub.
R / Bioconductor Suite including GenomicRanges and , Python, Emacs / ESS, whole genome
sequencing analysis, facility with clinical variant interpretation, familiarity with continuous integration / software
Qualified candidates must be able to effectively communicate with all levels of the organization.