Senior Data Scientist, Predictive Immune Biomarker Foundation Models

Senior Scientist, Predictive Immune Biomarkers (R3)

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.

• Foundation Models: Build, fine-tune, troubleshoot, and benchmark foundation models for predictive immune biomarker discovery.

• Multi-omics Analysis: Perform quality control (QC) and analysis of different molecular data types including bulk and single-cell RNA-seq (e.g., Limma, Seurat, scanpy), spatial transcriptomics (e.g., Cos Mx, 10x Visium), and proteomics (e.g., OLINK, mass spectrometry-based approaches).

• Multi-omics Integration: Integrate multi-omics datasets, including gene/protein expression, spatial transcriptomics, and genotype data.

• Documentation: Prepare detailed documentation of analysis methods and results in a timely manner.

• Ph.D. in Computer Science, Engineering, Data Science, AI/ML, Bioinformatics, Computational Biology, Genetics & Genomics, Mathematics, Statistics, Physics, Pharmaceutical Science, or related STEM field.

• A proven track record in multi-omics analysis.

• Hands-on experience with building, fine-tuning, benchmarking foundation models, preferably for patient stratification related downstream tasks.

• Fundamental understanding of AI/ML methods, multi-omics data analysis, and omics 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.

• Good understanding of autoimmune disease biology.

• Experience in processing and analyzing real-world data.

• Familiarity with spatial transcriptomics analysis.

• Knowledge of statistical and population genetics principles.

#EligibleforERP

Autoimmune Diseases, Biomarkers, Cellular Immunology, Computational Biology, Data Science, Genetic Analysis, High Performance Computing (HPC), Immunology Research, Machine Learning (ML), Omics, RNA-Seq, Stratification, Transcriptomics

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