data & validation manager

At 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

The 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.

The 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.

Reporting to the R&D 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 R&D Operations, ensuring that validation evidence is reusable, comparable, and fit for system-level and release-level decision-making.

The position combines technical expertise in data governance and statistical validation with organizational leadership, enabling the R&D Factory to operate with a shared, scalable, and reliable evidence pipeline across projects and product lifecycles.

KEY RESPONSIBILITIES

Dataset Governance & Quality

• Build, release, and maintain high-quality datasets and ground truth for AI training, validation, benchmarking, regression testing, and post-market activities.
• Lead dataset readiness workflows including selection, filtering, quality scoring, versioning, approval gates, and secure release to downstream users.
• Maintain gold-standard and reference datasets, ensuring representativeness, reproducibility, and strict train/validation/test separation to prevent data leakage.
• Forecast data needs in alignment with product and AI roadmaps, prioritizing dataset production pipelines accordingly.
• Oversee governance frameworks for data intake, curation, annotation, versioning, lineage, access control, and regulatory readiness across the full data lifecycle.
• Oversee and support the maintenance and evolution of data quality standards, including completeness, fidelity, annotation accuracy, integrity, stratification, and end-to-end traceability.

System Validation & Verification

• Contribute to the overall validation strategy required for the release of medical devices and platforms, including R&D tools and production-related tools, assessing where data- and system-level validation activities can effectively support broader validation efforts.
• Define validation strategies, methodologies, and performance metrics for AI systems, including performance verification criteria, regression strategies, and deployment consistency expectations.
• Lead 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.
• Develop and maintain statistical validation frameworks covering sampling, stratification, confidence intervals, power analysis, and lifecycle re-validation.
• Support integrated V&V workflows contributing to software, AI, and system-level release decisions.
• Oversee 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.

Regulatory & Quality Interface

• Ensure dataset documentation, validation protocols, execution outputs, and performance evidence meet applicable quality and regulatory requirements.
• Contribute dataset justifications, validation reports, and evidence packages for regulatory submissions (Pre-Subs, 510(k)/De Novo, and EU Technical Files).
• Ensure full alignment with cybersecurity, privacy, and data protection requirements across all data and validation operations.

Cross-Functional Collaboration

• Collaborate with AI, Software, Hardware & NPI, and Quality Engineering teams to ensure validated data, execution workflows, and validation outputs integrate effectively into system workflows.
• Partner with R&D Operations to define timelines, resource plans, and throughput targets for data and validation deliverables.
• Align data acquisition strategies with Clinical Affairs to support clinical evidence generation and multi-site data collection.
• Provide dataset insights, validation results, and risk-based assessments to R&D Factory leadership and Product Development teams.

Team & Capability Management

• Build, lead, and mentor a multidisciplinary team of data, annotation, and validation/test engineers and specialists.
• Define roles, responsibilities, and professional development paths for team members.
• Set and monitor KPIs for data quality, dataset readiness, validation throughput, and operational efficiency.
• Drive continuous improvement across annotation, dataset production, validation pipelines, and supporting tools and automation.

REQUIREMENTS

Education

• Degree in Engineering, Computer Science, Data Science or a related technical field; advanced degree preferred.

Experience

• 5+ years of experience in data management, system or AI/ML validation, V&V, or related roles within regulated MedTech or other high-reliability domains.
• Proven experience working across data acquisition, curation, annotation, quality control, and dataset release pipelines, in coordination with specialist roles.
• Demonstrated experience contributing to the validation of AI-enabled systems, including regression testing, performance verification, and comparability across versions.
• Experience with medical imaging or high-bandwidth video data pipelines, including representative data selection and ground truth considerations.
• Experience operating in matrix organizations, coordinating technical activities across multiple teams and stakeholders.
• Strong leadership, communication, and cross-functional collaboration capabilities.

Technical Knowledge

• Strong understanding of data quality principles, including stratification, representativeness, versioning, traceability, and bias control.
• Solid experience with statistical validation methods relevant to AI and system performance characterization (e.g., sampling strategies, confidence intervals, power analysis).
• Knowledge of AI/ML workflows, data-driven development, and validation/testing best practices.
• Familiarity with standardized execution, reproducibility concepts, and validation evidence generation across systems.
• Solid understanding of IEC 62304, ISO 13485, EU MDR, FDA software/AI guidance, and related regulatory expectations (preferred).
• Fluent spoken and written English.

Core Competencies

• Leadership: ability to guide multidisciplinary teams operating at the intersection of data and system validation.
• Analytical rigor: strong statistical and methodological competence for AI validation.
• Systems thinking: clear understanding of how data and validation contribute to a regulated AI/medical device ecosystem.
• Technical judgment: ability to evaluate dataset quality, validation outcomes, and associated risks.
• Execution discipline: dedication to quality, traceability, and regulatory alignment.
• Collaboration: effective coordination with clinical, AI, software, and product development stakeholders.

Physical Requirements:

Expected travel is 30%

Equal Opportunity Statement:

We support equal opportunities, without any discrimination; The research complies with Legislative Decree 198/2006