Computational Scientist (Contract Position)
About The Position
RTI’s Genomics and Applied Public Health group is seeking a skilled and motivated Computational Scientist. The successful candidate will provide dedicated scientific and analytical support to the National Cancer Institute’s Division of Cancer Epidemiology & Genetics through their expertise in tumor genomics, lung cancer biology, and epidemiology. They will advance the Sherlock-Lung Study, a large-scale initiative investigating the genomic, transcriptomic, and methylation landscapes of lung cancer in never smokers, as well as their spatial architecture, to uncover mutational processes, molecular changes, and tumor evolution. The successful candidate 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. This position centers on hypothesis-driven investigation that combines biological and computational expertise, with leadership in producing high-impact publications that advance understanding of lung cancer development and progression. This is a contract position anticipated to last up to one year, with the possibility of extension based on project needs and funding. There is a strong preference for this position to be based onsite at the National Cancer Institute (NCI) Shady Grove campus in Rockville, MD.
Requirements
- PhD degree and a minimum of two (2) years of progressively responsible relevant 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.
- Strong experience working with genomic databases such as TCGA, dbGaP, gnomAD, cBioPortal, ENCODE, 1000 Genomes, All of Us, GTEx, ICGC, PCAWG, and UK Biobank.
- Extensive proficiency in scripting and programming languages including Bash, R, and Python, with experience in RStudio, Jupyter Notebooks, and code management on GitHub.
- Significant experience with high-performance computing (HPC) environments and job scheduling systems such as SLURM.
- Proven experience preparing high-impact research manuscripts for peer-reviewed publications.
- Applicants must be legally authorized to work in the United States and should not require, now or in the future, sponsorship for employment visa status (e.g., H-1B visa status, etc.).
Nice To Haves
- Minimum of five (5) years of postdoctoral or equivalent experience in academia or industry.
- Strong written, verbal, and presentation skills. Ability to work effectively in a multidisciplinary research environment and communicate technical findings clearly to non-specialist audiences.
- Biological expertise in lung cancer with the ability to lead and drive large-scale projects.
- Hands-on experience with advanced tumor genomic analyses such as driver gene identification, mutational signature deconvolution, copy number phasing, tumor purity, microsatellite instability detection, and telomere length estimation.
- Expertise in integrative multi-omic analyses, including genomics, transcriptomics, epigenomics, and emerging spatial/single-cell modalities.
- Strong understanding of algorithmic efficiency and experience working in HPC and cloud environments to analyze population-scale datasets.
- Experience with various environment/dependency management tools (e.g. pip, venv, conda, renv), containerization with Docker/Singularity, and workflow management systems such as Snakemake or Nextflow.
- Proven ability to work both independently and collaboratively as part of a team.
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.
Benefits
- competitive base salary
- generous paid time off policy
- merit based annual increases
- bonus opportunities
- robust recognition program
- competitive range of insurance plans (including health, dental, life, and short-term and long-term disability)
- access to a retirement savings program such as a 401(k) plan
- paid parental leave for all parents
- financial assistance with adoption expenses or infertility treatments
- financial reimbursement for education and developmental opportunities
- employee assistance program
- numerous other offerings to support a healthy work-life balance
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
