Senior Translational & Computational Biologist (Hybrid AI)

Pfizer; New York (NY); Hybrid; USD 106,000 - 177,000; Full time

Benefits offered by this job

401(k) with Pfizer Matching

Paid vacation

Parental leave

Job summary

Pfizer seeks a Senior Translational and Computational Biologist to transform complex biological and clinical data into actionable scientific insights that guide drug discovery and development. You will work with AI scientists, translational researchers, clinicians, and therapeutic area experts to answer high‑value questions across multi-omics, imaging, and clinical datasets.

The role emphasizes developing domain expertise, independent analysis, and collaboration to shape scientific decisions

Qualifications

• Demonstrated success applying computational approaches to biological problems.
• Strong foundation in statistical methods and data interpretation.
• Experience translating biological questions into analytical workflows.

Responsibilities

• Independently conduct defined analyses across biological, molecular, genetic, clinical data.
• Summarize findings with clarity and rigor for scientific decisions.
• Generate hypotheses and identify meaningful biological patterns.
• Deliver evidence-based insights supporting scientific decision-making.
• Build domain expertise in disease biology and drug discovery processes.
• Learn to influence decisions through data-driven results.

Skills

R/Python

Machine learning

Data visualization

Statistical analysis

Education

PhD preferred in Computational Biology / related field

Master’s degree with 2+ years of relevant experience

Tools

AI/ML workflows

Biological data analysis

Job description

Pfizer seeks a Senior Translational and Computational Biologist to transform complex biological and clinical data into actionable scientific insights that guide drug discovery and development. You will work with AI scientists, translational researchers, clinicians, and therapeutic area experts to answer high‑value questions across multi-omics, imaging, and clinical datasets.

The role emphasizes developing domain expertise, independent analysis, and collaboration to shape scientific decisions