Computational Scientist II, NCI Sherlock-Lung

About the role

The Computational Scientist II will provide dedicated scientific and analytical support to the Integrative Tumor Epidemiology Branch (ITEB) through their expertise in tumor genomics, lung cancer biology, and epidemiology. They will lead integrative analyses and scientific interpretation of somatic high-coverage whole-genome sequencing (WGS) and multi-omics datasets from the Sherlock-Lung cohort, consisting of over 3,000 subjects.

Responsibilities

• Formulate and test biological hypotheses related to mutational processes, intra-tumor heterogeneity, clonal architecture, and evolutionary dynamics in lung cancer.
• Lead integrative analyses of somatic and germline variation (SNVs, indels, structural variants, copy number alterations), mutational signatures, and driver events using large-scale short-read and long-read WGS and multi-omics datasets.
• Apply advanced statistical approaches to extract insights from genomic datasets and synthesize findings with clinical and multi-omics data.
• Critically evaluate and implement emerging analytical methods for single-cell, spatial, and multi-omics analyses to enhance biological discovery.
• Ensure analytical rigor, reproducibility, and scalability of computational workflows.
• Lead and contribute to peer-reviewed publications, present findings at scientific meetings, and communicate results to multidisciplinary collaborators.
• Contribute to study design and analytic strategy for ongoing and future Sherlock-Lung initiatives.

Requirements

• Possession of a PhD degree from an accredited college or university according to the Council for Higher Education Accreditation (CHEA).
• Minimum of two (2) years of progressively responsible experience.
• Demonstrated experience and in-depth understanding of tumor genomics and cancer biology.
• Proven expertise in next-generation sequencing (NGS) data analysis and visualization using both custom and open-source bioinformatics tools, with a focus on somatic whole-genome sequencing analyses and multi-omics data integration.
• Proficiency with core statistical methods and modern machine learning approaches appropriate for high-dimensional genomic data, with emphasis on biological interpretability.
• Significant experience with high-performance computing (HPC) environments and job scheduling systems such as SLURM.
• Extensive proficiency in scripting and programming languages including Bash, R, and Python, with experience in RStudio, Jupyter Notebooks, and code management on GitHub.
• Extensive experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Strong experience working with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with high-performance computing (HPC) environments and job scheduling systems such as SLURM.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
• Significant experience with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes