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Phd Proteomics information

What is the difference between Phd Proteomics vs Proteomics Technician?

AspectPhd ProteomicsProteomics Technician
Required CredentialsPhD in Biochemistry, Molecular Biology, or related fieldAssociate's or Bachelor's degree in Life Sciences
Work EnvironmentResearch labs, academia, biotech companiesLaboratories, research facilities, industry settings
Job FocusDesigning experiments, data analysis, publishing researchSample preparation, instrument operation, data collection
Industry UsageAcademic research, pharmaceutical R&D, biotechLaboratory support roles in similar industries

In summary, Phd Proteomics involves advanced research, experimental design, and data analysis, requiring a doctoral degree. Proteomics Technicians focus on hands-on laboratory work and sample processing, typically with a bachelor's or associate degree. Both roles are essential in proteomics research but differ in responsibilities and educational requirements.

What are popular job titles related to Phd Proteomics jobs?

For Phd Proteomics jobs, the most frequently searched job titles are:

Infographic showing various Phd Proteomics job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 79% Physical, 4% Hybrid, and 17% Remote job distribution.

Senior Scientist - Bioinformatics, Computational Biology, Proteomics

South San Francisco, CA • On-site

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Posted 2 days ago

New

Job description

Responsibilities
  • Develop computational tools and data infrastructure for the Discovery Proteomics Group
  • Conduct statistical analysis, data ingestion, database and tool development, and computational pipeline implementation for proteomics data
  • Support biomarker discovery, target evaluation, experimental design, and mechanism-of-action studies
  • Develop and implement data analysis, management, and visualization strategies for complex mass spectrometry and other technology datasets
  • Work closely with scientists across functions and therapeutic areas
  • Deliver and present analysis results to key stakeholders
  • Collaborate across genomic and proteomic platforms for data integration and mining
  • Utilize available external resources where appropriate
  • Enable Amgen’s preclinical pipeline through innovative technologies and technical expertise
Requirements
  • Doctorate degree OR Master’s degree and 3 years of scientific experience OR Bachelor’s degree and 5 years of scientific experience
  • PhD in Computational Biology, Bioinformatics, Statistics, Proteomics, or Biology preferred
  • Expertise in large-scale quantitative proteomics and other omics data analysis
  • Fluency in Python, R, and SQL
  • Familiarity with analytic techniques/packages for statistical analysis, data processing, data ingestion, data visualization, and basic machine learning
  • Working knowledge of Unix/Linux and AWS cloud computing environments
  • Experience in version control, Docker containers, coding project management, reproducible data analysis, and documentation
  • Experience in meta-analysis with publicly available large proteomics studies is a plus
  • Strong background in biology or chemistry, especially human diseases in Immunology, Oncology, Inflammation, and Cardiometabolic Disease
  • Proven track record of independent critical thinking and scientific achievement
  • Strong verbal and written skills
  • Ability to work independently and in small teams
  • Willingness and ability to collaborate across scientific disciplines and cross-functional teams
Core Competencies

Demonstrates expertise in computational biology and proteomics, with strong capabilities in data analysis, management, and visualization. Proficient in Python, R, and SQL, with a solid understanding of statistical analysis and machine learning techniques.

Highest-signal resume keywords
  • PhD In Computational Biology
  • Large-Scale Quantitative Proteomics Analysis
  • Fluency In Python, R, And SQL
  • Experience In Docker Containers And Version Control
  • Strong Background In Biology Or Chemistry
Hard Skills
  • Statistical Analysis
  • Data Ingestion
  • Database Development
  • Computational Pipeline Implementation
  • Data Visualization
  • Analytic Techniques
  • Machine Learning
  • Reproducible Data Analysis
  • Meta-Analysis
  • Data Management
Soft Skills
  • Independent Critical Thinking
  • Strong Verbal Communication
  • Strong Written Communication
  • Collaboration Across Disciplines
  • Ability To Work In Small Teams
Industry Keywords
  • Proteomics
  • Biomarker Discovery
  • Target Evaluation
  • Experimental Design
  • Human Diseases
  • Immunology
  • Oncology
  • Inflammation
  • Cardiometabolic Disease
Tools & Technologies
  • Unix/Linux
  • AWS Cloud Computing
  • Docker
  • Version Control Systems
  • Analytic Packages
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