Senior Scientist, Cellular Genomics & Multi-Omics Data Science

Pfizer; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

Pfizer is seeking a highly productive senior scientist to join the Cellular Genomics group in the Inflammation and Immunology Research Unit. This role blends computational biology (80%) with wet-lab genomics (20%), focusing on high-dimensional single-cell, spatial, and multi-omics data to support drug development programs.

The candidate will apply rigorous workflows in R and Python, standard bioinformatics pipelines, and take part in study design, data-quality review, and limited hands-on

Qualifications

• BS/MS/PhD in relevant field with computational biology/genomics focus
• Experience analyzing single-cell, spatial, and multi-omics data
• Proficiency in R and Python, including statistics and visualization
• Experience with wet-lab genomics workflows and data quality assessment

Responsibilities

• Lead computational analysis of single-cell, spatial, and multi-omics datasets
• Translate high-dimensional genomic data into mechanistic hypotheses for drug development
• Apply established pipelines and workflows in collaboration with partners
• Integrate cellular genomics data with disease biology and pharmacology
• Provide wet-lab genomics input for study design and data-quality review

Skills

Computational biology

Genomics

Systems biology

Immunology

Bioinformatics

R

Python

Statistics

Data visualization

Quality control

Education

BS/MS/PhD in relevant field

Tools

Version-controlled workflows

Command-line tools

Job description

Pfizer is seeking a highly productive senior scientist to join the Cellular Genomics group in the Inflammation and Immunology Research Unit. This role blends computational biology (80%) with wet-lab genomics (20%), focusing on high-dimensional single-cell, spatial, and multi-omics data to support drug development programs.

The candidate will apply rigorous workflows in R and Python, standard bioinformatics pipelines, and take part in study design, data-quality review, and limited hands-on