Sr. Scientist, Predictive Immune Biomarkers Full-time NEW

Senior Scientist

The Precision Genetics group within the Data, AI and Genome Sciences Department is seeking a Senior Scientist to join our Computational Precision Immunology team in Cambridge, MA. We are looking for a skilled data scientist with extensive experience to develop predictive biomarkers in immunology based on multi-modal and multi-scale data analyses.

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.

Required Qualifications:

• Ph.D. in Computational Biology or a related field.
• A proven track record of over 5 years of hands-on experience 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 Services (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:

• Good understanding of auto-immune disease biology.
• Experience in processing and analyzing real-world data.
• Familiarity with spatial transcriptomics analysis.
• Knowledge of statistical and population genetics principles.

Required Skills:

Biomarkers, Computational Biology, Data Science, Genomics, High Performance Computing (HPC), Human Genetics, Machine Learning (ML), Omics, Precision Medicine (PM), RNA Sequencing

Preferred Skills:

Population Genetics, Real World Data, Statistical Genetics, Transcriptomics