Intelliswift Software
Bioinformatics Scientist II
Intelliswift Software, Cambridge, Massachusetts, United States, 02238
Job ID: 25-10081
Job Title:
Bioinformatics Scientist - II Duration:
23 months, 40 hrs / week
Location:
Cambridge, MA 02141
Qualifications:
Required Qualifications:
•Ph.D. in Computational Biology or a related field.
•A proven track record of over 3 years in multi-omics analysis.
•Fundamental 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 the ability to establish best practices for reproducible data analyses.
•Experience with high-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).
•A collaborative and self-motivated individual with a strong work ethic, capable of managing multiple objectives in a dynamic environment and adapting to changing priorities.
•Excellent written and verbal communication skills.
Preferred Qualifications:
•Experience in processing and analyzing real-world data.
•Familiarity with spatial transcriptomics analysis.
•Knowledge of statistical and population genetics principles.
Responsibilities:
Location: Cambridge, MA
Key Responsibilities:
•Data Ingestion: Query external databases to acquire relevant multi-omics datasets (e.g., PubMed, Gene Expression Omnibus, ArrayExpress, gnomAD, GTEx, Ensembl).
•RNA-seq Analysis: Perform quality control (QC) and analysis of bulk and single-cell RNA-seq data using state-of-the-art methods (e.g., FastQC, STAR, Limma, DESeq2, clusterProfiler, Seurat, scanpy, LeafCutter).
•Multi-Omics Analysis: Analyze diverse molecular data types including spatial transcriptomics (e.g., Slide-seq, MERFISH, squidpy) and proteomics (e.g., OLINK, mass spectrometry-based approaches).
•Data Integration: Integrate multi-omics datasets, including gene/protein expression, mRNA splicing, spatial transcriptomics, and genotype data.
•Documentation: Prepare detailed documentation of analysis methods and results in a timely manner.
Job Title:
Bioinformatics Scientist - II Duration:
23 months, 40 hrs / week
Location:
Cambridge, MA 02141
Qualifications:
Required Qualifications:
•Ph.D. in Computational Biology or a related field.
•A proven track record of over 3 years in multi-omics analysis.
•Fundamental 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 the ability to establish best practices for reproducible data analyses.
•Experience with high-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).
•A collaborative and self-motivated individual with a strong work ethic, capable of managing multiple objectives in a dynamic environment and adapting to changing priorities.
•Excellent written and verbal communication skills.
Preferred Qualifications:
•Experience in processing and analyzing real-world data.
•Familiarity with spatial transcriptomics analysis.
•Knowledge of statistical and population genetics principles.
Responsibilities:
Location: Cambridge, MA
Key Responsibilities:
•Data Ingestion: Query external databases to acquire relevant multi-omics datasets (e.g., PubMed, Gene Expression Omnibus, ArrayExpress, gnomAD, GTEx, Ensembl).
•RNA-seq Analysis: Perform quality control (QC) and analysis of bulk and single-cell RNA-seq data using state-of-the-art methods (e.g., FastQC, STAR, Limma, DESeq2, clusterProfiler, Seurat, scanpy, LeafCutter).
•Multi-Omics Analysis: Analyze diverse molecular data types including spatial transcriptomics (e.g., Slide-seq, MERFISH, squidpy) and proteomics (e.g., OLINK, mass spectrometry-based approaches).
•Data Integration: Integrate multi-omics datasets, including gene/protein expression, mRNA splicing, spatial transcriptomics, and genotype data.
•Documentation: Prepare detailed documentation of analysis methods and results in a timely manner.