Biotech Research And Development Jobs in Missouri

• Own the delivery of TA‑facing data products, translating scientific and analytics use cases into clear functional and technical requirements.
• Contribute to and execute a TA data product roadmap aligned with enterprise data strategy, spanning discovery, translational research, clinical development, and real‑world evidence.
• Lead or support agile product delivery, ensuring adherence to FAIR data principles (Findable, Accessible, Interoperable, Reusable).
• Drive integration and harmonization of internal and external data sources, including clinical trial data, omics, imaging, competitor intelligence, registries, and RWE datasets.
• Contribute to the development, curation, and application of TA ontologies to ensure semantic consistency across discovery, clinical, regulatory, and real‑world data.
• Align TA semantic models and metadata with enterprise standards and external frameworks (e.g., CDISC, GA4GH, FHIR, NCI Thesaurus).
• Partner with ontology, metadata, and governance teams to ensure data products are AI‑ready and semantically enriched.
• Partner closely with TA Data Science, clinical, translational, and scientific stakeholders to co‑design data products that maximize scientific and analytics value.
• Collaborate with Data Product Architecture & Governance, Knowledge Management, and Master Data Management teams to ensure seamless integration into enterprise knowledge graphs, catalogs, and platforms.
• Interface with IT, compliance, and regulatory partners to ensure data products meet quality, privacy, and global regulatory expectations.
• Provide day‑to‑day leadership and technical direction to product owners, data engineers, and TA data domain experts.
• Support the implementation of governance models, agile practices, and delivery standards for TA data products.
• Act as a coach and mentor, strengthening product, semantic, and data literacy capabilities within the team.
• Define and monitor product‑level KPIs to assess impact on scientific insights, analytics velocity, trial efficiency, and regulatory outcomes.
• Communicate progress, outcomes, and value realization to TA leadership, Data Science teams, and broader stakeholders.
• Actively champion adoption, reuse, and data literacy within the TA community.

• PhD or Master’s degree in Informatics, Computer Science, Data Science, Life Sciences, or a related discipline.
• 6–10+ years of experience in pharma/biotech R&D with a focus on data products, data engineering, analytics platforms, or data science.
• Demonstrated experience delivering domain‑specific data products within a Therapeutic Area or scientific domain.
• Hands‑on experience with multi‑modal data, including omics, imaging, translational, clinical trial data, and/or RWD/RWE.
• Strong understanding of data product design, data modeling, data integration, and data transformation.
• Familiarity with research, clinical development, and regulatory data requirements.
• Working knowledge of metadata management, ontologies, semantic models, and knowledge graphs.
• Experience aligning with industry and enterprise data standards (e.g., CDISC, GA4GH, FHIR).
• Strong collaboration, communication, and influence skills, with the ability to bridge scientific, technical, and business perspectives.
• Proven ability to drive adoption and change in a complex, matrixed organization.

Expertise in delivering data products within therapeutic areas, with a strong focus on data integration, modeling, and transformation. Proven ability to collaborate across scientific, technical, and business domains while adhering to industry standards and regulatory requirements.

• PhD Or Master’s Degree In Informatics
• 6–10+ Years Of Experience In Pharma/Biotech R&D
• Hands-On Experience With Multi-Modal Data
• Strong Understanding Of Data Product Design
• Experience Aligning With Industry Data Standards

• Data Product Delivery
• Data Engineering
• Data Integration
• Data Modeling
• Data Transformation
• Multi-Modal Data Analysis
• Clinical Trial Data Management
• Ontologies Development
• Metadata Management
• Semantic Models

• Collaboration
• Communication
• Influence
• Coaching
• Mentoring

• Therapeutic Area
• Real-World Evidence
• Clinical Development
• Regulatory Data Requirements
• CDISC
• GA4GH
• FHIR

• Knowledge Graphs
• Agile Practices
• Data Analytics Platforms
• FAIR Data Principles
• Enterprise Data Strategy