Data & Validation Manager

ppAt Cosmo, we are at the forefront of revolutionizing healthcare through our groundbreaking technology. With our innovative advancements in live endoscopy, we are transforming the landscape of medical diagnostics. We leverage AI to empower physicians, enabling them to make well-informed decisions and significantly improve the lives of their patients. Our dedication to excellence has resulted in creating GI Genius: our AI-enabled medical device for live endoscopy has received FDA approval and is successfully deployed in hospitals worldwide, making a significant impact in the field of healthcare. Check out our website for more information at /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. /ppThe role provides stewardship across data governance, dataset readiness, and system validation, working through specialist leads and senior engineers responsible for data collection, annotation, and test execution. The Manager ensures consistency, traceability, and alignment of data and validation outputs with product needs, validation strategies, and regulatory expectations, without replacing domain-specific ownership of methods and execution. /ppReporting to the RD Factory Director, the Data Validation Manager contributes to the definition and coordination of validation strategies, statistical methodologies, and performance metrics for AI systems, and supports their coherent application across software-defined medical devices, platforms, and supporting tools. The role acts as an integration point between Artificial Intelligence, Software Engineering, Hardware NPI, Clinical Affairs, Service Engineering, Product Development Quality, and RD Operations, ensuring that validation evidence is reusable, comparable, and fit for system-level and release-level decision-making. /ppThe position combines technical expertise in data governance and statistical validation with organizational leadership, enabling the RD Factory to operate with a shared, scalable, and reliable evidence pipeline across projects and product lifecycles. /ph3KEY RESPONSIBILITIES /h3h3Dataset Governance Quality /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. /liliOversee and support 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, assessing where data- and system-level validation activities can effectively support broader validation efforts. /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 /ulh3REQUIREMENTS /h3h3Education /h3ulliDegree in Engineering, Computer Science, Data Science or a related technical field; advanced degree preferred. /li /ulh3Experience /h3ulli5+ 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 /ulh3Technical Knowledge /h3ulliStrong 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 /ulh3Core Competencies /h3ullibLeadership: /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