Data & Validation Manager
Job Description & How to Apply Below
Job reference: 6228c
About the Company
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, which has received FDA approval and is successfully deployed in hospitals worldwide.
Job Overview
The Data & Validation Manager coordinates data lifecycle and validation activities within the Med Tech AI Division, ensuring that high‑quality, representative, and regulatory‑ready evidence supports the development, validation, and release of AI‑enabled medical technologies. The role steward ships data governance, dataset readiness, and system validation across interdisciplinary teams and ensures alignment with product needs, validation strategies, and regulatory expectations.
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.
Support the maintenance and evolution of data quality standards, including completeness, fidelity, annotation accuracy, integrity, stratification, and end‑to‑end traceability.
Contribute to the overall validation strategy required for the release of medical devices and platforms, assessing where data‑ and system‑level validation activities can 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…
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