Position: Context Engineer with pharmaceutical or life sciences domain experience

Location: Open to remote ( preferred Candidate to work in CST)

Duration:  Long term

We are looking for a Context Engineer with pharmaceutical or life sciences domain experience to help build and maintain structured, high-quality context for AI-driven applications. This role focuses on pharmaceutical commercial data, data engineering, semantic modeling, and mapping domain-specific terminology to standardized, preferred terms.

Key Responsibilities

• Analyze and engineer data sources relevant to pharmaceutical commercial operations and drug development.
• Build and maintain semantic layers, ontologies, taxonomies, and knowledge representations to support AI-driven applications.
• Map pharmaceutical entities, products, and terminology to standardized, normalized, preferred terms.
• Work with pharmaceutical controlled vocabularies and ontologies to improve data consistency, discoverability, and interoperability.
• Support entity resolution, terminology mapping, data normalization, and semantic data integration across multiple sources, particularly within a commercial setting.
• Collaborate with data engineering, analytics, and AI teams to improve data quality and context for downstream applications.
• Help structure domain-specific information to support accurate and reliable AI retrieval and responses.

Required Qualifications

• Experience in biopharmaceutical data, particularly in an omnichannel marketing setting.
• Strong understanding of pharmaceutical terminology, controlled vocabularies, and data standards related to drug development and commercialization domains.
• Hands-on experience with one or more of the following:
• SNOMED CT
• MedDRA
• RxNorm
• Experience with data engineering, data modeling, entity resolution, terminology mapping, or data normalization.
• Familiarity with ontologies, taxonomies, knowledge graphs, or semantic layers.
• Ability to work with cross-functional teams, including Analytics, Data Engineering, and AI/ML.

Preferred Qualifications

• Experience with pharmaceutical commercial data, product data, reference data, or master data management.
• Exposure to AI/ML, Generative AI, RAG, semantic search, or context engineering.
• Experience integrating data from multiple sources and aligning entities to standardized terminology.

 

Ideal Candidate

The ideal candidate combines pharmaceutical domain knowledge with strong data and semantic modeling skills. Experience with scientific research is beneficial but not the primary focus; familiarity with pharmaceutical commercial terminology and controlled vocabularies is especially important.  Interested candidates are encouraged to apply or connect to learn more about the opportunity.