Principal Scientist, Data Science - R&D

Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing Therapeutics Development & Supply data Create and optimize structured and unstructured data flows using Python, R, SQL, DBT, cloud services, and modern engineering tools Develop and maintain TDS-specific data repositories and enterprise-level data models Ensure data is structured, versioned, traceable, and semantically aligned for AI/ML readiness Translate business needs into high-quality data products and engineering requirements with data scientists, domain experts, and digital technology teams Implement semantic models and future-proof data architectures with ontology and knowledge graph teams Define data quality and performance standards and KPIs for accuracy, completeness, and consistency Apply data versioning and lineage tracking for compliance, traceability, and audit readiness Follow software development best practices, including code versioning, DevOps integration, and documentation Engage scientific, technical, and operations stakeholders to understand requirements, design solutions, and drive adoption Support multiple concurrent projects and manage priorities across the TDS network Requirements Advanced degree in Engineering, Data Science, Life Sciences, Computer Science, or related field 3+ years of experience in data engineering, including data modeling and database design Proficiency with Python, R, SQL, and cloud-based architectures such as AWS services, Snowflake, and Redshift Experience with NoSQL and graph databases Strong analytical, problem-solving, and stakeholder-management skills Ability to translate discussions into actionable requirements Ability to drive multiple projects simultaneously with strong organizational skills and adaptability Preferred: experience with regulated or standards-driven data environments such as

CDISC

, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards Preferred: familiarity with high-dimensional data such as imaging and sensor data Preferred: experience connecting to or feeding MLOps and model deployment workflows Preferred: knowledge of manufacturing systems, laboratory information systems, or industrial data systems Preferred: experience with knowledge graph architectures Core Competencies Demonstrates expertise in designing and maintaining scalable data pipelines, ensuring data quality and compliance while collaborating with cross-functional teams to deliver high-quality data products. Proficient in utilizing modern engineering tools and cloud architectures to support data-driven decision-making in Therapeutics Development & Supply. Highest-signal resume keywords Data Engineering Python Programming SQL Proficiency Cloud-Based Architectures Data Modeling ATS Optimization Keywords Hard Skills Data Pipeline Design Data Integration Data Repository Development Data Versioning Data Lineage Tracking Database Design NoSQL Databases Graph Databases Data Quality Standards AI/ML Readiness Soft Skills Analytical Skills Problem-Solving Stakeholder Management Organizational Skills Adaptability Certifications & Qualifications Advanced Degree in Engineering Data Science Life Sciences Computer Science Industry Keywords Therapeutics Development Regulated Data Environments CDISC HL7 FHIR OMOP

DICOM

Manufacturing Standards Knowledge Graph Architectures High-Dimensional Data Tools & Technologies AWS Services Snowflake Redshift DBT DevOps Integration #J-18808-Ljbffr Jobtailor