Data & Validation Manager

The Data & Validation Manager is responsible for coordinating and integrating data lifecycle and validation activities within the Med Tech 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.

Responsibilities and Scope 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, including R&D tools and production-related tools.

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.

Qualifications and Requirements Education Degree  in Engineering, Computer Science, Data Science or a related technical field; advanced degree preferred.

Experience5+ years of experience in data management, system or AI/ML validation, V&V, or related roles within regulated Med Tech 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…