Apply by 29 October 2026
Turn complex data into scientific discoveries and public health impact.
What can large clinical and genomic datasets tell us about infectious diseases? Can we identify emerging antimicrobial resistance patterns earlier? How can statistical modelling and machine learning help us understand disease transmission, clinical outcomes, and public health interventions?
At the Child Health Research Foundation (CHRF), we work at the intersection of data science, infectious diseases, genomics, and public health to investigate questions that matter for children’s health. We are seeking a skilled and intellectually curious Data Scientist who enjoys solving complex analytical problems and wants to contribute to scientific research with national and global relevance. You will collaborate with multidisciplinary teams to transform complex datasets into robust analyses and meaningful scientific evidence.
Why join CHRF -
1. Work on important scientific challenges: Contribute to research involving infectious diseases, antimicrobial resistance, vaccines, and child health.
2. Work across disciplines: Collaborate with scientists in epidemiology, microbiology, genomics, and clinical research.
3. Contribute intellectually: Help research teams choose analytical approaches, interpret complex findings, and develop new methods.
4. Build a meaningful scientific career: Gain experience in collaborative research at an institution committed to strengthening scientific capacity in Bangladesh.
Location: Dhaka, Bangladesh
How to apply -
Please email hr@chrfbd.org by 29 October 2026 with your CV (maximum 2 pages) and a brief cover letter (maximum 1 page) describing why you are interested in working at CHRF and the technical strengths and experiences you would bring to this position. Candidates are encouraged to describe relevant analytical projects, research experience, or programming work. Please use “Data Scientist” in the email subject line.
Only shortlisted candidates will be contacted.
CHRF is an equal opportunity employer. We welcome qualified applicants regardless of race, color, religion, sex, gender, or disability status.
Who we are looking for -
Essential qualifications and skills
1. A master’s degree in Statistics, Mathematics, Computer Science, Data Science, Bioinformatics, Engineering, or a related quantitative discipline.
2. Strong knowledge of statistical analysis, quantitative research methods, and analytical problem solving.
3. Proficiency in at least one programming language used for data analysis, such as Python, R, or Stata.
4. Experience working with complex datasets and developing reliable analytical workflows.
5. Ability to independently investigate data, select appropriate analytical approaches, and
communicate findings.
6. Strong written and spoken English and an ability to collaborate with multidisciplinary teams.
Additional skills that would be an advantage
1. Two or more years of professional or research experience in data science, statistics, machine learning, or quantitative analysis.
2. Experience analyzing clinical, epidemiological, or other public health datasets.
3. Experience with predictive modelling, machine learning, advanced statistical methods, or data visualization.
4. Familiarity with SQL, version control, and reproducible computational workflows.
5. Experience contributing to scientific publications, technical reports, or collaborative research projects.
Previous experience in public health is an advantage, but not essential. We are particularly interested in candidates who demonstrate strong technical competence, analytical independence, curiosity, and a willingness to work across disciplines.
Salary: Negotiable based on qualifications and experience Benefits: Contributory provident fund, festival allowances (Eid, Puja, Christmas, and Baishakhi), and a two day weekend.
What you will do -
1. Lead and contribute to the analysis of complex clinical, epidemiological, laboratory, and
surveillance datasets.
2. Design and implement appropriate statistical analyses, predictive models, and machine learning approaches to address research questions.
3. Develop efficient and reproducible workflows for data processing, quality assurance, analysis, and visualization.
4. Explore patterns and relationships within large datasets and critically assess the robustness and limitations of findings.
5. Evaluate and improve model performance, accuracy, and reliability.
6. Collaborate with scientists and epidemiologists to translate research questions into rigorous analytical approaches.
7. Present findings through clear visualizations, scientific reports, and presentations for technical and nontechnical audiences.
8. Contribute analytical findings to research outputs and reports for collaborators and funding partners.
9. Support other members of the data science team and contribute to CHRF’s Building Scientists for Bangladesh initiative.
10. Identify opportunities to introduce new tools, methods, and computational approaches that strengthen CHRF’s research.