Principal Scientist, Data Science (Data Products, Integration & Analysis)

About the role

The Principal Scientific Data Scientist will lead the design, implementation, and evolution of scientific data products and integration strategies supporting AI-enabled drug discovery and development.

Responsibilities

• Define and execute a scientific data product strategy supporting discovery research, translational science, preclinical safety, clinical development, pharmacovigilance, and real-world evidence.
• Create scalable, interoperable, and AI-ready data products that connect discovery, preclinical, clinical, safety, and real-world evidence domains, and enable the creation of validated-biomarker data assets.
• Establish the data architecture, integration strategy, metadata framework, and productization approach needed to support semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI applications.
• Work closely with scientific stakeholders, knowledge architects, AI engineers, and Amazon BioDiscovery platform teams to define the future-state scientific data ecosystem and ensure high-quality data products are delivered to support translational science and patient safety initiatives.
• Build AI reasoning models to support data-driven translational safety decision making.

Requirements

• Education: Master’s or PhD in Computer Science, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or related scientific discipline.
• Experience: 5+ years of experience in scientific data engineering, data architecture, data products, or life sciences informatics.
• Technical Expertise: Strong expertise in data architecture, data modeling, data product design, cloud-native data platforms, metadata management, data governance, and predictive model development.
• Experience: Experience supporting drug discovery, development, clinical research, or pharmacovigilance organizations.
• Preferred Qualifications: Experience supporting knowledge graphs, semantic architectures, or GraphRAG initiatives; experience building AI-ready data products and feature stores; familiarity with ontology-driven data integration approaches; experience partnering with cloud providers or external platform teams; experience operating in highly regulated scientific environments.

Qualifications

• Strategic thinker capable of defining long-term data product roadmaps.
• Strong communicator who can bridge scientific and technical communities.
• Ability to influence cross-functional teams without direct authority.
• Strong execution focus with a bias toward scalable, reusable solutions.
• Passion for transforming biomedical R&D through data, AI, and modern engineering practices.