RESPONSIBILITIES

• Significant understanding of Immunology, Oncology, Cardiovascular Diseases.
• Identify and implement state-of-the-art statistical methods for data exploration, visualization, analysis, and integration of cancer genomics/epi-genomics, and other forms of high-dimensional - omics data for patient clinical outcomes
• Develop and implement computational pipelines using Python, R, Bash, and AWS to analyze NGS data (RNAseq, WES, WGS, MRD, ctDNA, CRISPR) for tasks like tumor load distribution, MRD status, immune cell analysis, and variant calling
• Substantial experience in analyzing WES, RNA-seq, and ctDNA datasets
• Evaluate the performance of NGS assays (sensitivity, accuracy, concordance)
• Utilize machine learning and statistical analysis to identify clinically relevant insights from NGS data, including associations between gene expression, IHC markers, immune cell populations, and clinical outcomes
• Develop and evaluate performance of existing or new assays through statistical inferences
• Interpret quality control data metrics in NGS methodology and communicate effectively with the team and the respective stakeholders
• The individual to work closely with the laboratory teams while supporting analysis of external vendor evaluations and pilot studies
• Collaborate closely with others on translational research teams to evaluate, develop, and apply cutting-edge methods for analysis of multi-modal, high-dimensional -omics data
• Excellent written and oral communication skills, including an ability to converse with computational scientists, experimentalists, and clinicians.
• Familiarity with preclinical and clinical trial data analysis.
• Extensive experience analyzing and interpreting NGS data.
• Hands-on experience with relevant public domain data sets including 1KG, Exac, GnomAD, and TCGA.
• Fluency with cloud and Linux based high performance compute environments, R/Bioconductor and reproducible research practices.
• Communicate effectively to build support for work and align with organization

REQUIREMENTS

• Ph.D. with 1-5 yrs experience.
• Expertise in algorithmic implementation, statistical programming, and data manipulation, using e.g., R or Python, and contemporary, open-source bioinformatics tools and database structures
• Solid grounding in statistical theory and familiarity with recent developments in statistics
• Skilled at working with large omics data sets (transcriptomic, genomic, proteomic, and/or epigenomic data) Understanding of cancer genomics and epi-genomics is required
• Proficient with high-performance computing environments like cloud computing (AWS)
• Working knowledge of workflow languages for example: CWL or Nextflow
• Working knowledge of web frameworks like R Shiny or Django
• Communicate effectively to build support for work and align with organization

PREFERENCES

• Expertise in algorithmic implementation, statistical programming, and data manipulation, using e.g., R or Python, and contemporary, open-source bioinformatics tools and database structures
• Solid grounding in statistical theory and familiarity with recent developments in statistics
• Skilled at working with large omics data sets (transcriptomic, genomic, proteomic, and/or epigenomic data) Understanding of cancer genomics and epi-genomics is required

LOCATION

HOW TO APPLY

DEADLINE