Data Research Analyst

Insight Global is hiring for a Research Data Owner for a leading pharmaceutical client. The RDO will provide enterprise-level leadership, governance, and strategic direction for high‑value research datasets used across the Data, Automation & Predictive Sciences organization. Acting as the central authority for lifecycle management, risk acceptance, and consumption of foundational scientific data, the RDO ensures shared cross‑domain datasets are trustworthy, compliant, well curated, and optimized for advanced analytics, automation, and predictive modeling.

This highly collaborative role partners with scientific, technical, and governance stakeholders to drive data quality, interoperability, FAIR/ALCOA+++ compliance, and responsible reuse across the biopharmaceutical R&D ecosystem. Key responsibilities include overseeing strategic data ownership and stewardship; leading governance, regulatory alignment, and policy enforcement; enabling cross‑functional collaboration with data architects, business data owners and stewards, and research technology teams; managing the end‑to‑end data lifecycle from acquisition through retirement;

championing continuous improvement of standards and processes; and communicating strategic decisions, risks, and data value across the organization.

• Bachelor’s or Master’s degree in Bioinformatics, Computational Biology, Data Science, Life Sciences, or related field.
• Experience in research data governance, data stewardship, or scientific data management within biopharma, biotech, or academic research.
• Strong understanding of FAIR data principles, data lifecycle management, and regulatory considerations for research data.
• Familiarity with public scientific data repositories and foundational biological datasets.
• Demonstrated ability to work across scientific, technical, and governance functions.
• Excellent communication skills with the ability to translate complex data concepts into actionable guidance.

• Knowledge of ontologies, controlled vocabularies, and semantic data modeling.
• Understanding of cloud‑based data platforms, data engineering workflows, and modern analytics ecosystems.
• Experience supporting AI/ML, automation, or predictive modeling use cases.