Lead Data Manager

Short Description- The Laboratory for Intelligent Global Health and Humanitarian Technologies (LiGHT) at EPFL is seeking a Lead Data Manager to build and oversee all data management activities in the Lab. This is a unique opportunity to shape the lab’s data architecture, pipelines, and governance from the ground up, supporting major AI-for-health initiatives and complex multinational clinical trials.

** Mission**-
- It is based at the **Swiss Institute of Technology (EPFL)** - In the top 10 computer science institutions globally, EPFL is a deeply international hub for technical excellence in AI.- Our mission is to develop safe, scalable, and context-aware digital health tools. We work with international NGOs such as WHO, MSF, ICRC, and national ministries of health to design, create, validate and deploy trustworthy AI in real-world healthcare systems—especially in settings where clinical resources are limited and stakes are high.

** Main duties and responsibilities**- ** Strategic Leadership & Governance**- Lead and oversee all Data Management (DM) activities across LiGHT’s research portfolio, including complex multicountry clinical trials in Africa and AI-based health innovation projects.-
- Develop, implement, and continuously refine the LiGHT Data Management and Data Governance framework, integrated within the EPFL ecosystem and compliant with GCP, GDPR, DPDP (India) and relevant data protection regulations.-
- Act as the focal point and accountable lead for all data management and compliance-related matters in LiGHT’s role as Sponsor or Coordinating Centre within international consortia.-
- Develop and maintain all SOPs, work instructions, templates, and forms related to data management, biostatistics, and data governance.-
- **Data Architecture & Infrastructure**- Design, implement, and maintain secure, scalable data architectures for clinical and AI research projects, including EDC systems, data lakes, and integration pipelines.-
- Oversee the design & integration of eCDS solutions and ensure interoperability across databases, AI model repositories, and analytics environments.-
- Liaise with EPFL IT and research data offices to ensure alignment with institutional standards and data security policies.-
- **Data Science & AI Integration**- Lead the development of data pipelines for AI model training, validation, and deployment, including data curation, annotation, quality assurance, and metadata governance.-
- Develop data-driven dashboards, monitoring systems, and automated pipelines for data analysis and visualization.-
- Support the development of AI-ready datasets, ensuring proper data provenance, consent tracking, and harmonization across sites.-
- Work closely with data scientists and engineers on machine learning model governance, versioning, and reproducibility.-
- **Operational Oversight & Mentorship**- Coordinate and supervise contractual data managers, data scientists, and external CROs or IT partners, ensuring timely and high-quality deliverables.-
- Develop and maintain contractual and quality oversight mechanisms, including vendor assessment and performance tracking.-
- Mentor students, junior researchers, and collaborators in data management, reproducible research, and ethical data practices.-
- Support scientific reporting, donor communications, and publications based on LiGHT datasets.-
- **Partnerships & Innovation**- Explore and establish strategic collaborations with external partners in academia, industry, and global health to strengthen LiGHT’s data science ecosystem.-
- Stay abreast of emerging trends in AI, data standards (e.g., CDISC), FAIR data principles, and federated learning for global health research.

**Profile**- **Required**- PhD (preferred) or Master’s degree in data science, computer science, biomedical informatics, or a related quantitative field-
- Minimum 5 years of experience in data management, data engineering, or applied data science, including leadership in global health or clinical research.-
- Demonstrated experience managing distributed data workflows in LMIC and multi-institutional research environments.-
- Proven proficiency in all aspects of clinical data management, including database setup, CRF design, edit check programming, data validation, query management, SAE reconciliation, coding, data review, and database lock.-
- Experience establishing and maintaining Data Quality Management Systems (QMS) for research environments.-
- Proven experience managing data management subcontracts and oversight of external vendors or CROs.-
- Strong understanding of data versioning, lineage tracking, and reproducibility standards, including Git-based workflows and CI/CD integration.-
- Proficiency in Python-
- Familiarity with CDISC, GxP standards, and international research data compliance with a strong foundation in data security and compliance, including GDPR and emerging frameworks like India’s DPDP Act (2023).-
- Demonstrated contribution to open-source or FAI