PpThe Data Validation Manager is responsible for coordinating and integrating data lifecycle and validation activities within the MedTech AI Division, ensuring that high-quality, representative, and regulatory-ready evidence is available to support the development, validation, and release of AI-enabled medical technologies. /ph3Responsibilities and Scope /h3ulliBuild, release, and maintain high-quality datasets and ground truth for AI training, validation, benchmarking, regression testing, and post-market activities. /liliLead dataset readiness workflows—including selection, filtering, quality scoring, versioning, approval gates, and secure release to downstream users. /liliMaintain gold-standard and reference datasets, ensuring representativeness, reproducibility, and strict train/validation/test separation to prevent data leakage. /liliForecast data needs in alignment with product and AI roadmaps, prioritizing dataset production pipelines accordingly. /liliOversee governance frameworks for data intake, curation, annotation, versioning, lineage, access control, and regulatory readiness across the full data lifecycle. /liliSupport the maintenance and evolution of data quality standards, including completeness, fidelity, annotation accuracy, integrity, stratification, and end-to-end traceability. /liliContribute to the overall validation strategy required for the release of medical devices and platforms, including RD tools and production-related tools. /liliDefine validation strategies, methodologies, and performance metrics for AI systems, including performance verification criteria, regression strategies, and deployment consistency expectations. /liliLead the execution of validation activities for AI models, software components, and integrated systems across embedded, cloud, and real-time environments, ensuring alignment with the defined validation strategy. /liliDevelop and maintain statistical validation frameworks covering sampling, stratification, confidence intervals, power analysis, and lifecycle re-validation. /liliSupport integrated VV workflows contributing to software, AI, and system-level release decisions. /liliOversee the definition and adoption of standardized system execution outputs and test session structures to ensure validation results are reproducible, comparable, and reusable across projects and system versions. /li /ulh3Regulatory Quality Interface /h3ulliEnsure dataset documentation, validation protocols, execution outputs, and performance evidence meet applicable quality and regulatory requirements. /liliContribute dataset justifications, validation reports, and evidence packages for regulatory submissions (Pre-Subs, 510(k)/De Novo, and EU Technical Files). /liliEnsure full alignment with cybersecurity, privacy, and data protection requirements across all data and validation operations. /li /ulh3Cross-Functional Collaboration /h3ulliCollaborate with AI, Software, Hardware NPI, and Quality Engineering teams to ensure validated data, execution workflows, and validation outputs integrate effectively into system workflows. /liliPartner with RD Operations to define timelines, resource plans, and throughput targets for data and validation deliverables. /liliAlign data acquisition strategies with Clinical Affairs to support clinical evidence generation and multi-site data collection. /liliProvide dataset insights, validation results, and risk-based assessments to RD Factory leadership and Product Development teams. /li /ulh3Team Capability Management /h3ulliBuild, lead, and mentor a multidisciplinary team of data, annotation, and validation/test engineers and specialists. /liliDefine roles, responsibilities, and professional development paths for team members. /liliSet and monitor KPIs for data quality, dataset readiness, validation throughput, and operational efficiency. /liliDrive continuous improvement across annotation, dataset production, validation pipelines, and supporting tools and automation. /li /ulh3Qualifications and Requirements /h3pbEducation /b /pulliDegree in Engineering, Computer Science, Data Science or a related technical field; advanced degree preferred. /li /ulpbExperience /b /pulli5+ years of experience in data management, system or AI/ML validation, VV, or related roles within regulated MedTech or other high-reliability domains. /liliProven experience working across data acquisition, curation, annotation, quality control, and dataset release pipelines, in coordination with specialist roles. /liliDemonstrated experience contributing to the validation of AI-enabled systems, including regression testing, performance verification, and comparability across versions. /liliExperience with medical imaging or high-bandwidth video data pipelines, including representative data selection and ground truth considerations. /liliExperience operating in matrix organizations, coordinating technical activities across multiple teams and stakeholders. /liliStrong leadership, communication, and cross-functional collaboration capabilities. /li /ulpbTechnical Knowledge /b /pulliStrong understanding of data quality principles, including stratification, representativeness, versioning, traceability, and bias control. /liliSolid experience with statistical validation methods relevant to AI and system performance characterization (e.g., sampling strategies, confidence intervals, power analysis). /liliKnowledge of AI/ML workflows, data-driven development, and validation/testing best practices. /liliFamiliarity with standardized execution, reproducibility concepts, and validation evidence generation across systems. /liliSolid understanding of IEC 62304, ISO 13485, EU MDR, FDA software/AI guidance, and related regulatory expectations (preferred). /liliFluent spoken and written English. /li /ulpbCore Competencies /b /pullibLeadership /b: ability to guide multidisciplinary teams operating at the intersection of data and system validation. /lilibAnalytical rigor /b: strong statistical and methodological competence for AI validation. /lilibSystems thinking /b: clear understanding of how data and validation contribute to a regulated AI/medical device ecosystem. /lilibTechnical judgment /b: ability to evaluate dataset quality, validation outcomes, and associated risks. /lilibExecution discipline /b: dedication to quality, traceability, and regulatory alignment. /lilibCollaboration /b: effective coordination with clinical, AI, software, and product development stakeholders. /li /ulh3Physical Requirements /h3pExpected travel is 30% /ph3Equal Opportunity Statement /h3pWe support equal opportunities, without any discrimination; The research complies with Legislative Decree 198/2006 /p /p #J-18808-Ljbffr