Required Qualifications: • Ph.D. in Computational Biology or a related field. • A proven track record of over 5 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. Note: • Onsite role at Cambridge, MA. • Ph.D. candidates only. Key Skills: • Required expertise with multi-omics data analysis and integration (e.g., RNA-Seq, single-cell RNA-Seq. • Transcriptomics analysis. • Proficiency in R and Bash. • High-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets). Responsibilities: Location: Cambridge, MA Department: Data and Genome Sciences Group: Precision Genetics The Precision Genetics group within the Data and Genome Sciences Department is seeking a skilled Contractor to join our Computational Precision Immunology team. We are looking for a data scientist with extensive experience in multi-modal and multi-scale data analyses to contribute to our innovative research efforts. 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.