Senior Information Architect, Life Sciences Data & Ontologies

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

Benefits offered by this job

Competitive salary

Comprehensive benefits

Hybrid work environment

Job summary

Merck & Co. seeks a Senior Information Architect to lead design, organization, and standardization of scientific data across pre-clinical discovery and manufacturing processes.

You will partner with scientists, engineers, and IT to harmonize biological, chemical, and operational data—from raw outputs to GxP-compliant data lakes. You will define ontologies, data models, and controls, working with industry-standard ontologies to contextualize datasets and improve data quality.

Qualifications

• Bachelor's or Master's in Life Sciences, CS, Bioinformatics, Information Science, or related field.
• 5+ years as an Information Architect in Biopharma or Life Sciences.
• Experience with ontologies, RDF/OWL/SPARQL, and knowledge graphs.
• Ability to define data models and reusable data products for diverse needs.
• Familiarity with LIMS, ELN, CDS, and lab endpoint software.

Responsibilities

• Understand stakeholders' data needs and translate into information architecture.
• Embed in product teams to enable capture and consumption of scientific data; define ontologies and data models.
• Define standards and controls for data and metadata to improve quality and context at capture.
• Lead adoption of industry ontologies (BAO and others) to contextualize datasets and parameters.
• Maintain best practices for information capture and consumption across research processes.

Skills

Ontology knowledge

RDF/OWL/SPARQL

Data modeling

Stakeholder engagement

Biopharma data

Education

Bachelor's or Master's

Tools

CENTree

Job description

Merck & Co. seeks a Senior Information Architect to lead design, organization, and standardization of scientific data across pre-clinical discovery and manufacturing processes.

You will partner with scientists, engineers, and IT to harmonize biological, chemical, and operational data—from raw outputs to GxP-compliant data lakes. You will define ontologies, data models, and controls, working with industry-standard ontologies to contextualize datasets and improve data quality.