Bioinformatics Scientist - II

Title: Bioinformatics Scientist - II
Contract Duration: 24 months
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

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

Note:
* Onsite role at Cambridge, MA
* Do not submit candidates who are looking for remote
* Do not submit candidates with just BS/MS