Intelliswift
Job Title:
Bioinformatics Scientist - III Duration:
23 months, 40 hrs / week Location:
Cambridge, MA 02141 Qualifications:
Required Qualifications: Ph.D. in Computational Biology or a related field. Over 5 years of experience in multi-omics analysis. Strong understanding of statistical methods and multi-omics data analysis and integration (e.g., RNA-Seq, single-cell RNA-Seq, genotype, spatial transcriptomics, OLINK). Proficiency in R, Python, and Bash, with a focus on reproducible data analyses. Experience with HPC systems and AWS Cloud Computing (e.g., IAM, S3). Collaborative, self-motivated, with a strong work ethic and adaptability. Excellent communication skills. Preferred Qualifications: Experience processing and analyzing real-world data. Familiarity with spatial transcriptomics analysis. Knowledge of statistical and population genetics principles. Key Skills: Expertise in multi-omics data analysis and integration. Transcriptomics analysis. Proficiency in R and Bash. Experience with HPC and AWS Cloud. Responsibilities:
Key Responsibilities: Data ingestion from external databases (e.g., PubMed, GEO, ArrayExpress, gnomAD, GTEx, Ensembl). Perform QC and analysis of bulk and single-cell RNA-seq data using tools like FastQC, STAR, Limma, DESeq2, Seurat, scanpy, LeafCutter. Analyze diverse molecular data, including spatial transcriptomics and proteomics. Integrate multi-omics datasets, including gene/protein expression, splicing, spatial transcriptomics, and genotype data. Document analysis methods and results thoroughly and promptly.
#J-18808-Ljbffr
Bioinformatics Scientist - III Duration:
23 months, 40 hrs / week Location:
Cambridge, MA 02141 Qualifications:
Required Qualifications: Ph.D. in Computational Biology or a related field. Over 5 years of experience in multi-omics analysis. Strong understanding of statistical methods and multi-omics data analysis and integration (e.g., RNA-Seq, single-cell RNA-Seq, genotype, spatial transcriptomics, OLINK). Proficiency in R, Python, and Bash, with a focus on reproducible data analyses. Experience with HPC systems and AWS Cloud Computing (e.g., IAM, S3). Collaborative, self-motivated, with a strong work ethic and adaptability. Excellent communication skills. Preferred Qualifications: Experience processing and analyzing real-world data. Familiarity with spatial transcriptomics analysis. Knowledge of statistical and population genetics principles. Key Skills: Expertise in multi-omics data analysis and integration. Transcriptomics analysis. Proficiency in R and Bash. Experience with HPC and AWS Cloud. Responsibilities:
Key Responsibilities: Data ingestion from external databases (e.g., PubMed, GEO, ArrayExpress, gnomAD, GTEx, Ensembl). Perform QC and analysis of bulk and single-cell RNA-seq data using tools like FastQC, STAR, Limma, DESeq2, Seurat, scanpy, LeafCutter. Analyze diverse molecular data, including spatial transcriptomics and proteomics. Integrate multi-omics datasets, including gene/protein expression, splicing, spatial transcriptomics, and genotype data. Document analysis methods and results thoroughly and promptly.
#J-18808-Ljbffr