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Tier-1 employer and metro location increase competition, but niche knowledge-graph expertise reduces applicant pool.
Knowledge-graph and pharma regulatory focus increases domain specificity, though data architecture skills remain transferable.
Mandatory 5–8 years plus specialized knowledge-graph, Databricks, SPARQL, and compliance requirements creates strict filters.
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Design, develop, and deploy connected data solutions integrating information modeling, knowledge graphs, and Generative AI to address complex pharmaceutical operational challenges.
Translate ambiguous business requirements into scalable AI-enabled data products using structured and unstructured data across enterprise platforms like AWS, Databricks, and OpenAI APIs.
Ensure compliance with responsible AI practices, regulatory frameworks, and data governance while promoting continuous improvement in AI product delivery and business adoption.
Master’s or Bachelor’s degree with 5 to 8 years of experience in Data Science, AI, Computer Science, Information Science, or related fields (Doctorate degree also acceptable).
Strong hands-on experience with information modeling, knowledge graph development, Generative AI applications, and SQL for data integration and transformation.
Experience with data/AI platforms such as Databricks and knowledge graph platforms like Stardog or GraphDB.
Knowledge of FAIR data principles and compliance with regulatory requirements including familiarity with AI regulations such as the EU AI Act.
Technically strong and strategically minded data practitioner capable of advancing proof-of-concept AI solutions to enterprise-scale implementations in regulated pharmaceutical environments.
Experienced collaborator able to engage cross-functional stakeholders to translate business needs into innovative AI and data-driven solutions.
Experienced in integrating pharmaceutical and operational data domains with an emphasis on regulatory, manufacturing, quality, and operational data use cases.