Senior Information Architect - Biopharma Data & Ontologies

Merck; Boston (MA); Hybrid; USD 142,000 - 224,000; Full time

Benefits offered by this job

Hybrid work arrangement

Competitive salary

Job summary

Merck is seeking a Senior Information Architect to lead design and standardisation of scientific data across pre-clinical discovery and manufacturing processes. You will work with scientists to define information constructs, ontologies, and data models to improve data quality and context.

The role requires building data architectures with stakeholders and integrating industry-standard ontologies to contextualise datasets and manufacturing parameters, enabling reusable data products for diverse

Qualifications

• Experience in BEA, data architecture, and data modelling for biopharma processes.
• Ability to translate scientific needs into data models and ontologies.
• Familiarity with semantic web technologies and knowledge graphs.
• Experience collaborating with scientists, engineers, and IT teams.

Responsibilities

• Understand key outcome needs from data and translate into architectures.
• Define ontologies and data models to support data capture and reuse.
• Lead standardisation and controls for data and metadata at capture.
• Adopt industry-standard ontologies (e.g., BAO) for contextualising datasets.
• Develop best practices for information capture and data state across processes.

Skills

BEA

Business Process Modeling

Data Management

Data Modelling

Data Science

Requirements Management

Stakeholder Management

Strategic Planning

Education

Bachelor’s or Master’s in Life Sciences/CS/Bioinformatics/Info Science

Tools

CENTree

Job description

Merck is seeking a Senior Information Architect to lead design and standardisation of scientific data across pre-clinical discovery and manufacturing processes. You will work with scientists to define information constructs, ontologies, and data models to improve data quality and context.

The role requires building data architectures with stakeholders and integrating industry-standard ontologies to contextualise datasets and manufacturing parameters, enabling reusable data products for diverse