Senior Data Scientist, Predictive Immune Biomarkers
Merck
August 21, 2026
On-site
Cambridge, MA
Clinical Research and Development
Key Responsibilities:
- Build, fine-tune, troubleshoot, and benchmark foundation models for predictive immune biomarker discovery.
- Perform QC and analysis of bulk and single-cell RNA-seq (e.g., Limma, Seurat, scanpy), spatial transcriptomics (e.g., CosMx, 10x Visium), and proteomics (e.g., OLINK, mass spectrometry-based approaches).
- Integrate multi-omics datasets, including gene/protein expression, spatial transcriptomics, and genotype data.
- Prepare timely, detailed documentation of analysis methods and results.
Required Qualifications:
- Ph.D. in Computer Science, Engineering, Data Science, AI/ML, Bioinformatics, Computational Biology, Genetics & Genomics, Mathematics, Statistics, Physics, Pharmaceutical Science, or related STEM field.
- Proven track record in multi-omics analysis.
- Hands-on experience building, fine-tuning, and benchmarking foundation models, preferably for patient stratification.
- Fundamental understanding of AI/ML methods, multi-omics data analysis, and omics integration (RNA-Seq, single-cell RNA-Seq, genotype, spatial transcriptomics, OLINK).
- Proficiency in R, Python, and Bash; ability to establish reproducible analysis best practices.
- Experience with HPC systems and AWS Cloud Services (e.g., IAM, S3).
- Collaborative, self-motivated; able to manage multiple objectives and adapt to changing priorities.
- Excellent written and verbal communication skills.
Preferred Qualifications:
- Understanding of autoimmune disease biology.
- Experience processing/analyzing real-world data.
- Familiarity with spatial transcriptomics analysis.
- Knowledge of statistical and population genetics principles.
Skills (role-related): Autoimmune diseases, biomarkers, cellular immunology, computational biology, data science, genetic analysis, HPC, immunology research, machine learning, omics, RNA-seq, stratification, transcriptomics.
- Build, fine-tune, troubleshoot, and benchmark foundation models for predictive immune biomarker discovery.
- Perform QC and analysis of bulk and single-cell RNA-seq (e.g., Limma, Seurat, scanpy), spatial transcriptomics (e.g., CosMx, 10x Visium), and proteomics (e.g., OLINK, mass spectrometry-based approaches).
- Integrate multi-omics datasets, including gene/protein expression, spatial transcriptomics, and genotype data.
- Prepare timely, detailed documentation of analysis methods and results.
Required Qualifications:
- Ph.D. in Computer Science, Engineering, Data Science, AI/ML, Bioinformatics, Computational Biology, Genetics & Genomics, Mathematics, Statistics, Physics, Pharmaceutical Science, or related STEM field.
- Proven track record in multi-omics analysis.
- Hands-on experience building, fine-tuning, and benchmarking foundation models, preferably for patient stratification.
- Fundamental understanding of AI/ML methods, multi-omics data analysis, and omics integration (RNA-Seq, single-cell RNA-Seq, genotype, spatial transcriptomics, OLINK).
- Proficiency in R, Python, and Bash; ability to establish reproducible analysis best practices.
- Experience with HPC systems and AWS Cloud Services (e.g., IAM, S3).
- Collaborative, self-motivated; able to manage multiple objectives and adapt to changing priorities.
- Excellent written and verbal communication skills.
Preferred Qualifications:
- Understanding of autoimmune disease biology.
- Experience processing/analyzing real-world data.
- Familiarity with spatial transcriptomics analysis.
- Knowledge of statistical and population genetics principles.
Skills (role-related): Autoimmune diseases, biomarkers, cellular immunology, computational biology, data science, genetic analysis, HPC, immunology research, machine learning, omics, RNA-seq, stratification, transcriptomics.