Senior Data Manager

Aidence; Paris; Sur place; EUR 85 000 - 110 000; Plein temps

Résumé du poste

Aidence in Paris, France is seeking a Senior Data Manager to own data topics across products, including dataset creation, data export, annotation workflows, and performance monitoring.

You will design tooling and pipelines, collaborate with radiologists, PMs, AI engineers, clinical researchers, and data engineers to support medical product development, and mentor the data team to improve quality and efficiency.

Qualifications

• 5+ years of relevant experience in data management, data engineering, or data science, ideally in healthcare or medical imaging.
• Master’s degree in a health-related or scientific field.
• Excellent proficiency in Python and database query languages, and comfortable with BigQuery and MongoDB.

Responsabilités

• Own one or several data topics across products and drive them end to end.
• Design and maintain reusable tooling, pipelines, dashboards, and automation.
• Surface actionable insights from the data lake for stakeholders.
• Mentor junior data team members and contribute to code quality and reviews.
• Collaborate with radiologists, PMs, AI engineers, and data engineers to support medical product development.

Connaissances

Analytical mindset

Attention to detail

Strong reasoning

Mentor others

Formation

Master’s degree in health-related or scientific field

Outils

Python

BigQuery

MongoDB

Description du poste

• The Senior Data Manager is responsible for owning key data topics across products, including dataset creation, data export, annotation workflows, and performance monitoring, and for driving those topics end to end.
• This role makes structuring decisions on team tooling and data architecture, builds reusable pipelines and automation, and mentors more junior data team members.
• Work is done with radiologists, product managers, AI engineers, clinical research scientists, and data engineers to support medical product development
• Own one or several data topics across products, such as dataset creation, data export, annotation workflows, and performance monitoring, and drive them end to end
• Design and maintain reusable tooling, including pipelines, dashboards, and automation, for the data team and for stakeholders who need to access, explore, or use the data
• Make structuring decisions on technical implementation, team tooling, and data architecture, and maintain codebase quality and process efficiency
• Surface actionable insights from the data lake by identifying, structuring, and delivering relevant information to product, AI, and clinical stakeholders
• Drive the targeting and collection of relevant data
• Build and structure medically consistent and representative datasets
• Establish annotation guidelines and training so annotation quality stays consistent
• Contribute to ongoing oversight and refinement of AI algorithm performance
• Mentor more junior data team members through technical guidance and knowledge sharing
• Write automated tests and apply code versioning and review standards to data processes and tools

• Knowledge of medical imaging and of how data and annotations shape AI algorithm development (required)
• Preferred: Experience with medical device developmentStrong reasoning, attention to detail, and an analytical and scientific mindset (required)
• Ability to manage multiple topics in parallel while maintaining a broader view of product and data priorities (required)
• Ability to structure, document, industrialize, and optimize data processes and tools (required)
• Ability to translate needs between technical and less technical stakeholders and to mentor others (required)
• Experience writing automated tests and working with code versioning and review (required)
• Preferred: Machine learning understanding5+ years of relevant experience in data management, data engineering, or data science, ideally in healthcare or medical imaging (required)
• Master’s degree in a health-related or scientific field (required)
• Excellent proficiency in Python and database query languages, and comfort working with databases such as BigQuery and MongoDB (required)