Senior Scientist, Molecular Biology Data & Digital Workflows

Amgen; Washington (District of Columbia); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Total Rewards Plan

Stock-based incentives

Flexible work models

Retirement plan

Medical, dental and vision coverage

Paid time off

Job summary

Amgen seeks a Science Data Product Owner for molecular biology design data. You will define data models and workflows for plasmid design across biologics discovery, translating biology needs into scalable digital solutions.

You will partner with scientists, software engineers, and data teams to implement data models, integrate tools, and drive adoption of standardized data practices to accelerate research and discovery.

Qualifications

• PhD or equivalent in a relevant field.
• Experience with plasmid design workflows and cloning in biotech environments.
• Experience translating scientific workflows into informatics tools and data models.

Responsibilities

• Lead scientific requirements for data models and workflows.
• Clarify data and workflow requirements to software engineers and analysts.
• Partner with biology, data, and software teams to implement data models and integrations.
• Oversee release readiness, testing, and user adoption of data tools.
• Define standard practices for capturing high-quality molecular design data.

Skills

Molecular cloning

Plasmid design

Biologics discovery

Scientific data modeling

Agile software delivery

Requirements gathering

Data integration

Cross-functional collaboration

Communication

Product ownership

Education

Doctorate in Molecular Biology or related field

Master’s degree + 2 years experience

Bachelor’s degree + 4 years experience

Job description

Amgen seeks a Science Data Product Owner for molecular biology design data. You will define data models and workflows for plasmid design across biologics discovery, translating biology needs into scalable digital solutions.

You will partner with scientists, software engineers, and data teams to implement data models, integrate tools, and drive adoption of standardized data practices to accelerate research and discovery.