Experteer Overview In this role you will lead data quality across R&D Data Science Digital Health, shaping governance and standards to ensure accurate, traceable data for research and regulatory submissions. You will partner with cross-functional teams to implement enterprise quality guidelines and advance data validation, automation, and reporting. You will influence data stewardship and risk management while mentoring junior analysts. This position offers impact at scale within a mission-driven, innovation-focused organization.Compensaciones / Beneficios - Develop and maintain a data quality management framework aligned with GxP, FAIR, and enterprise principles - Define data quality dimensions (accuracy, completeness, consistency, timeliness, validity, integrity, and lineage) and establish KPIs - Collaborate with Data Governance, Knowledge Management Global Development, Regulatory and MDM teams to ensure traceability and alignment of data quality standards - Contribute to data stewardship by defining ownership and escalation procedures for quality issues - Lead data validation across multiple data types (clinical, real world, omics, manufacturing, digital health) and drive automation and monitoring - Design dashboards and reports to convey key metrics to scientists and leadership - Lead root cause analyses for data issues and oversee remediation plans - Collaborate with AI/ML, Real World Evidence, Clinical Data Management, Global Regulatory, J&J Technology, Epidemiology, and Commercial teams to translate quality controls into technical rules and workflows - Ensure data quality processes comply with regulatory and industry standards (FDA 21 CFR Part 11, GDPR, HIPAA, ALCOA+) and support audit readiness - Drive adoption of AI-assisted data quality tools and metadata-driven automation;
stay updated on trends - Mentor junior quality analystsResponsabilidades - 8–10+ years in pharma/biotech R&D or commercial functions with significant data management exposure, or 6+ years with an advanced degree - Experience in master data management, ontology development, and/or knowledge graphs - Understanding of controlled vocabularies (e.G., SNOMED CT, MedDRA, LOINC) - Proficiency in data profiling and validation;
experience with SQL, Python and/or R - Experience integrating data across discovery, development, clinical, regulatory, and/or commercial domains - Strong understanding of data standardization and harmonization - Experience working in validated environments - Knowledge of FDA and EMA guidelines, GxP, ALCOA+ data integrity, and 21 CFR Part 11 compliance - Awareness of CDISC, SDTM, ADaM, HDF5 and H5mu standards - Strong leadership, communication, and cross-functional collaboration;
strong writing for reports and SOPsRequisitos principales -