Data Scientist (Bioinformatics) for CODA Democra-MITS project

The Barcelona Institute for Global Health (ISGlobal) is a cutting-edge institute addressing global public health challenges through research, translation into policy and education. ISGlobal has a broad portfolio in communicable and non-communicable diseases including environmental and climate determinants, and applies a multidisciplinary scientific approach ranging from the molecular to the population level. Research is organized in five programs: Climate, Air Pollution, Nature and Urban Health; Environment and Health over the Lifecourse; Global Viral and Bacterial Infections; Malaria and Neglected Parasitic Diseases and Maternal Child and Reproductive Health. ISGlobal is accredited with the Severo Ochoa distinction, a seal of excellence of the Spanish Science Ministry.

WHAT WE ARE LOOKING FOR

ISGlobal is seeking a highly motivated and skilled Data Scientist (Bioinformatics) to join the Democra-MITS project, an international research initiative aimed at simplifying and scaling postmortem postmortem diagnostic method to improve cause-of-death determination in low-resource settings. We are looking for a candidate with strong quantitative and analytical skills, capable of managing and modeling large, heterogeneous biomedical datasets, and with a genuine interest in applying data science to global health challenges.

The ideal candidate combines technical expertise in bioinformatics, statistics, and machine learning with an understanding of biological and clinical data. The position requires creativity, attention to detail, and the ability to work effectively within a multidisciplinary team of pathologists, microbiologists, clinicians, and epidemiologists. A proactive attitude, problem-solving mindset, and commitment to scientific rigor and open science principles are essential. 

KEY RESPONSIBILITIES

Phase 1 - Retrospective Modeling and Data Integration

• Lead the curation, cleaning, and harmonization of large, multi-source biomedical datasets (clinical, histopathology, microbiology) derived from the CHAMPS, CaDMIA/CaDMIA-Plus, and São Paulo studies.
• Develop and implement AI-assisted data preprocessing pipelines and algorithms for automated data extraction, normalization, and validation.
• Apply statistical and machine learning models to identify high-yield diagnostic variables and optimize tissue and test selection for simplified MITS (s-MITS) protocols.
• Conduct model validation and performance evaluation using gold-standard autopsy datasets (CDA), generating metrics such as sensitivity, specificity, kappa, and AUC.
• Collaborate with pathologists, microbiologists, and clinicians to interpret analytical results and integrate them into evidence-based diagnostic frameworks.
• Prepare technical documentation, visual reports, and scientific summaries to support protocol refinement and stakeholder communication.

Phase 2 - Prospective Validation and Multicenter Data Management

• Support the setup and harmonization of multicenter data collection systems during the prospective validation phase in Mozambique and Nepal.
• Implement automated data monitoring tools and consistency checks to ensure data integrity across sites.
• Participate in the analysis of within-case comparisons between simplified MITS (s-MITS), conventional MITS (c-MITS), and complete diagnostic autopsy (CDA) results.
• Contribute to the generation of data visualizations, dashboards, and analytical reports for internal and external dissemination.
• Collaborate in scientific publications, presentations, and consortium meetings (e.g., CODA, CHAMPS, and MITS Alliance).
• Provide technical mentorship and support to junior analysts, trainees, or partner site data teams as required.

AUXILIAR TASKS

This job description reflects the present requirements of the post but may evolve at any time in the future as duties and responsibilities change and/or develop providing there is appropriate consultation with the post-holder.

This job description is not a definitive or exhaustive list of responsibilities but identifies the key responsibilities and tasks of the post holder. The specific objectives of the post holder will be subject to review as part of the individual professional assessment process.

SKILLS

• Solid understanding of biomedical data structures, including clinical, microbiological, and pathology datasets.
• Strong capacity for critical thinking and scientific reasoning, particularly in interpreting model outputs in biological and epidemiological contexts.
• Experience in data visualization and preparation of analytical reports for technical and non-technical audiences.
• Attention to detail and a rigorous approach to data quality assurance and documentati