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.
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.
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.
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 documentation.
Familiarity with data protection principles and good practices for managing sensitive health information.
Ability to manage multiple tasks and deadlines in a dynamic, research-driven environment.
Motivation to apply computational tools to real-world public health problems, contributing to improved disease surveillance and evidence-based policy making.
Enthusiasm for working at the interface between data science, medicine, and global health, and commitment to ISGlobal’s mission to improve health equity worldwide.
The post holder will adhere to ISGlobal principles contained in People management policy, including Equity, diversity and health safety. The post holder will respect, and be accountable to ensure ISGlobal policies and procedures.
Required:
PhD or MSc degree (with at least 3 years of relevant experience) in Bioinformatics, Computational Biology, Data Science, Biostatistics, or a related quantitative discipline.
Proven experience in data analysis and modeling of biomedical or epidemiological datasets, including integration of heterogeneous data types (clinical, microbiological, histopathological, or genomic).
Strong programming and analytical skills in Python and/or R, including proficiency with data wrangling, statistical modeling, and visualization packages.
Demonstrated expertise in artificial intelligence and machine learning, including supervised and unsupervised modeling, feature selection, and model validation.
Familiarity with version control and reproducible research tools (Git/GitHub, Jupyter, R Markdown, or similar).
Strong organizational and problem-solving skills, with capacity to work independently and collaboratively in a multidisciplinary team.
Previous experience in global health research, mortality surveillance, or clinical/pathology data analysis.
Knowledge of epidemiological methods and understanding of public health data systems in low- and middle-income countries (LMICs).
Experience with database management and REDCap or similar electronic data capture platforms.
Background in AI applications for health, including interpretability and performance evaluation of diagnostic models.
Track record of contributing to scientific publications or collaborative research projects.
Proven ability to work effectively within multidisciplinary teams, collaborating with experts in pathology, microbiology, epidemiology, and global health.
Proven capacity to lead technical innovation, proposing and implementing new analytical strategies and AI-based methodologies to enhance data quality, model performance, and scientific impact within the project framework.
Capacity to take initiative and ownership of analytical workflows, ensuring accuracy, transparency, and timely delivery of results.
Strong communication and collaboration skills, with the ability to translate complex technical concepts into clear language for diverse audiences.
Demonstrated problem-solving attitude and flexibility to adapt analytical approaches to evolving project needs.
Ability to contribute constructively to project meetings, mentor junior staff or students when needed, and maintain a cooperative working environment.
Proven capacity to lead technical innovation, proposing and implementing new analytical strategies and AI-based methodologies to enhance data quality, model performance, and scientific impact within the project framework.
Applicants must complete the application form and upload a CV and a Cover Letter. Each document must be clearly named with the candidate’s name and surname.
Applications will remain open for a minimum of 15 days from the date of publication. The position will stay open until it is filled and may be closed once a suitable candidate is identified.
Applications will be reviewed on a rolling basis, and shortlisted candidates may be contacted and interviewed as applications are received. Interviews may take place during the application period.
We encourage diverse applications, including but not limited to gender, race, ethnicity, religion, age, sexual orientation, physical abilities, and political beliefs.
The selection process is designed in two phases:
1- Interview phase of a technical nature, with the team that requires the incorporation. To assess the person's skills and CV.
2 - Meeting with HR with the finalist(s) to finish assessing the profile and discuss contractual and institutional issues.
If needed any technical test could be passed. A Psychological Competency Evaluation Test will be required for the structural or transversal positions.
In accordance with the OTM-R principles, a gender-balanced recruitment panel is formed for every vacancy at the beginning of the process. After reviewing the content of the applications, the panel will start the interviews, with at least one technical and one administrative interview. A profile questionnaire as well as a technical exercise may be required during the process.
In ISGlobal we are committed to maintaining and developing a work environment in which the values and principles of our organization are respected and equal opportunities between women and men be promoted in each of the areas in which we operate, not tolerating discrimination based on criteria such as age, gender, marital status, race, ethnicity, functional diversity, political leanings, religion, sexual orientation, gender identity or gender expression.
We confirm our commitment towards the value of the diversity of our staff and student population and seek to promote peace, equity, diversity and inclusion as essential elements in contribution to improving health worldwide.
