Job Id: P1336048BOSJB_1684777623

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Our client is seeking a dedicated Computational Biologist to join their Data Science Analytics team. The selected individual will support genetically defined disease programs across our portfolio. The role will focus on building, maintaining computational methodologies and performing the analysis of omics data from and publicly available biological databases. This is a great opportunity to be part of a team who delivers data-driven and practical insight by leveraging computational science from preclinical to late clinical development stage.

Key Responsibilities

• Develop and perform innovative computational analysis of multimodal data and deliver practical insight to advance our programs.
• Build internal analytical capabilities for DNAseq, RNAseq, scRNAseq, and other omics data
• Support for the integration heterogenous phenotypic and genomic datasets produced internally and from public repositories.
• Perform analysis to
  • leverage genotype phenotype data
  • perform association studies and on complexes phenotype and genotype data
  • extract and leverage clinical & demographic information (e.g: feature extraction)
• Keep a keen eye on emerging statistical genomic methodologies
• Ability to work both independently and collaboratively with a team.
• Collaborate cross functionally to facilitate interpretation and deliver practical insight from the data analyzed
• The ideal candidate will be detail oriented and enjoy data wrangling and ETL

Minimum Requirements

• PhD degree in computational biology, statistics, or related field; or a related master's degree with at least 5+ years of experience
• Demonstrated record of handling Next generation sequencing data.
• Experienced in statistical genomics: Bayesian inference, Meta-analysis and summary statistics, Heritability and mixed models, Mendelian randomization
• Experience with population-scale genetics analysis: GWAS, Relatedness and population structure
• Experience in conventional NGS analysis tools: BCFtools, VCFtools, PLINK, Regenie, GATK, bedtools, etc.
• Technical proficiency and experience in languages such are R, Python
• Strong written and verbal communication skills and a strong sense of accountability and scientific rigor is a must

Preferred Qualifications

• Experience with cohort phasing, polygenic risk scores and Genotype imputation is highly preferred
• Experience in the analysis of electronic health record data a strong plus
• Experience using AWS-backed cloud computing platforms, and preferably DNAnexus
• Familiarity with common public 'omics data repositories (such as Gene Expression Omnibus, NCBI databases, Ensembl, dbGAP etc.)

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