MEL Associate, Program Analytics and Learning Unit (PALU)

Summary

MCD Global Health is a recognized leader in healthcare and public health program development. The MEL Associate will support the design and implementation of the PALU framework, focusing on data automation, cleaning, and analytics to enhance project decision-making and support field teams.

Responsibilities

• Analyze and transform program data using R and/or Python to generate actionable insights and support decision-making
• Develop and maintain ETL pipelines, dashboards, and automated reporting solutions in collaboration with the DHTIS Lead
• Design, build, test, and maintain digital data collection tools, including ODK/XLSForms and DHIS2 forms
• Monitor data quality, conduct data validation, and ensure compliance with MCD MEL standards and donor requirements
• Support field teams through technical assistance, training, and the development of guidance materials to strengthen data use
• Collaborate with cross-functional teams to integrate MEL priorities into program implementation and business development activities
• Contribute to proposal development, technical reports, presentations, and organizational learning initiatives
• Identify opportunities to improve data workflows, automation, and digital systems

Skills

• Bachelor's degree in a quantitative field (e.g., Public Health, Epidemiology, Data Science, Statistics) or related discipline
• Practical experience (1–3 years) in MEL, data analysis, or related roles in global health or international development (internships or volunteer roles may be considered)
• Demonstrated, hands-on proficiency coding in R and/or Python is essential—specifically the ability to write reproducible scripts to automate repetitive data workflows, build ETL pipelines, and clean and transform raw data. Candidates should be able to show working examples (e.g., code samples, a repository, or a portfolio of automated tools)
• Demonstrated ability to build and deploy digital data collection forms using ODK (XLSForm) is required
• Communication & Teamwork: Effective at liaising with technical and non-technical colleagues; able to communicate complex data insights succinctly
• Detail Orientation: Skilled at managing large datasets, ensuring accuracy, and catching inconsistencies
• Adaptability & Problem-Solving: Comfortable navigating evolving priorities and problem-solving in fast-paced or multicultural environments
• Capacity to Learn: Demonstrates eagerness to acquire new technical skills and take on progressively more complex responsibilities
• Cross-Cultural Sensitivity: Values diversity and is capable of working respectfully across different contexts
• Proficiency in R and/or Python
• Master's degree preferred (e.g., MPH, MSc in Biostatistics, or similar), especially with coursework in research methods and advanced analytics
• Familiarity with JIRA, Confluence, or other project management platforms a plus
• Experience with statistical analysis (regression, modeling) is advantageous but not required; this role prioritizes applied coding and automation over advanced inferential statistics
• Basic knowledge of data visualization tools (e.g., Power BI, Tableau) is a plus
• Understanding of MEL framework design (e.g., log frames, theory of change, indicator tracking) preferred
• Working knowledge of languages, other than English, is a plus, particularly French and Spanish

Qualifications

Must Haves

• Bachelor's degree in a quantitative field (e.g., Public Health, Epidemiology, Data Science, Statistics) or related discipline
• Practical experience (1–3 years) in MEL, data analysis, or related roles in global health or international development (internships or volunteer roles may be considered)
• Demonstrated, hands-on proficiency coding in R and/or Python is essential—specifically the ability to write reproducible scripts to automate repetitive data workflows, build ETL pipelines, and clean and transform raw data. Candidates should be able to show working examples (e.g., code samples, a repository, or a portfolio of automated tools)
• Demonstrated ability to build and deploy digital data collection forms using ODK (XLSForm) is required
• Communication & Teamwork: Effective at liaising with technical and non-technical colleagues; able to communicate complex data insights succinctly
• Detail Orientation: Skilled at managing large datasets, ensuring accuracy, and catching inconsistencies
• Adaptability & Problem-Solving: Comfortable navigating evolving priorities and problem-solving in fast-paced or multicultural environments
• Capacity to Learn: Demonstrates eagerness to acquire new technical skills and take on progressively more complex responsibilities
• Cross-Cultural Sensitivity: Values diversity and is capable of working respectfully across different contexts
• Proficiency in R and/or Python

Nice to Haves

• Master's degree preferred (e.g., MPH, MSc in Biostatistics, or similar), especially with coursework in research methods and advanced analytics
• Familiarity with JIRA, Confluence, or other project management platforms a plus
• Experience with statistical analysis (regression, modeling) is advantageous but not required; this role prioritizes applied coding and automation over advanced inferential statistics
• Basic knowledge of data visualization tools (e.g., Power BI, Tableau) is a plus
• Understanding of MEL framework design (e.g., log frames, theory of change, indicator tracking) preferred
• Working knowledge of languages, other than English, is a plus, particularly French and Spanish

Benefits

• Remote position requiring virtual collaboration across global time zones.
• International travel (up to 5–10%) may be required to provide field support, conduct trainings, or attend project meetings.