Computational Scientist

Collaborate with scientists in Human Genetics department to analyze large datasets of genetic, genomic, and clinical data from internal studies (including our clinical trials and high throughput screens), collaborations with academic and industry partners, and public external data sets. We are particularly interested in candidates with skill sets that position them to tackle the integration of multiple sources of human biological data, such as whole genome sequencing and single cell RNA-Seq/ATAC-Seq data, including knowledge of emerging multimodal data integration methods.

Our client develops, manufactures and supplies a wide array of innovative medical diagnostic products, services, tests, platforms and technologies.

 

Project Overview:

Our client is seeking a highly independent computational scientist with a strong hands-on analytical background in genetic epidemiology, statistical genetics, or computational biology, to develop and apply analytical approaches to integrate and interpret genetic, genomic, and clinical data. We are particularly interested in candidates with skill sets that position them to tackle the integration of multiple sources of human biological data, such as whole genome sequencing and single cell RNA-Seq/ATAC-Seq data, including knowledge of emerging multimodal data integration methods.
 
Key Responsibilities:

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  Collaborate with scientists in Human Genetics department to analyze large datasets of genetic, genomic, and clinical data from internal studies (including our clinical trials and high throughput screens), collaborations with academic and industry partners, and public external data sets
  
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  Develop analytical approaches to integrate and interpret these data, delivering insights into disease biology to propel our translational goals
  
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  Implement novel machine learning algorithms to understand associations between imaging and omics data
  
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  Coordinate the intake and preparation of new datasets as they become available for analysis
  
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  Document process, findings, and code
  
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  Present findings to the department and cross-functional collaborators and contribute to publications
  

Qualifications:

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  Extensive experience in large-scale genetic and genomic data analysis, with expertise in one or more of the following areas:
  
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    Principles and applications of genetic epidemiology.
    
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    Association analysis of array- and sequence-based genetic data, including genome-wide association studies (GWAS) using human genetic data.
    
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    Analysis of sequence-based molecular assay data, including RNA sequencing (RNA-seq) and differential expression analysis.
    
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    Analysis of single-cell sequencing data, such as single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq), and/or proteomic data.
    
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    Integration of genetic and molecular data for multimodal analyses.
    
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  PhD, or a Master's degree with significant relevant experience, in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related quantitative or biological field.
  
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  Strong programming skills in R, Python, and shell scripting. Familiarity with C++ is an advantage.
  
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  Experience using Git for version control and working in high-performance computing (HPC) environments, including job scheduling systems such as SLURM.
  
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  Demonstrated curiosity and enthusiasm for learning more about human genetics, bioinformatics, and biology.
  
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  Ability to independently produce high-quality, reproducible analytical results with minimal supervision, while meeting project milestones and deadlines and making sound, data-driven decisions.
  
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  Strong written and verbal communication skills, with demonstrated ability to collaborate effectively within multidisciplinary and cross-functional teams.