MEL Associate, Program Analytics and Learning Unit (PALU)

We envision a world in which all people have access to high quality and enduring solutions to improve and maintain their health and well-being. MCD Global Health aspires to be a premier partner of choice and a recognized leader, innovator, and trusted partner in healthcare and public health program development, implementation, and evaluation. Our mission is to improve the health and well-being of people worldwide through enduring, high-quality, cost-effective, and universally accessible public health solutions. MCD operates impactful programs both in the U.S.A. and internationally.

The Monitoring, Evaluation, and Learning (MEL) Associate supports the design, implementation, and continuous improvement of the PALU framework across MCD International’s portfolio. The MEL Associate reports directly to the PALU SPM to contribute to the automation of data workflows, dashboard development, data cleaning, and descriptive analytics for project teams. This is a hands-on builder role: the emphasis is on writing reliable, replicable code to automate processes, turn raw field data into usable outputs, and ship working tools quickly. While the SPM sets the position’s priorities, the Associate also functions as a shared technical resource—providing automation and pipeline support to the DHTIS Lead, dashboard and descriptive-analysis support to the MEL Lead, and data-cleaning and form-building support to project staff.

 In this role, the MEL Associate assists in troubleshooting and strengthening data systems, supports the development of data collection tools, and ensures these tools are used adequately by field teams to guarantee organizational MEL standards. The position also collaborates with other units, including the PIU, the TAIU, and the BDU, to integrate evidence-based insights into project decision-making and proposal development.

• 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.

• Fast-paced environment balancing independent analytical work with close coordination under supervision.

• Prolonged computer use for data analysis and virtual meetings.

• Willingness to maintain a flexible schedule to accommodate diverse global teams.

• Capacity to travel to remote or resource-limited settings as needed.

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  Analyze and transform program data using R and/or Python to generate actionable insights and support decision-making.

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  Develop and maintain ETL pipelines, dashboards, and automated reporting solutions in collaboration with the DHTIS Lead.

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  Design, build, test, and maintain digital data collection tools, including ODK/XLSForms and DHIS2 forms.

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  Monitor data quality, conduct data validation, and ensure compliance with MCD MEL standards and donor requirements.

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  Support field teams through technical assistance, training, and the development of guidance materials to strengthen data use.

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  Collaborate with cross-functional teams to integrate MEL priorities into program implementation and business development activities.

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  Contribute to proposal development, technical reports, presentations, and organizational learning initiatives.

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  Identify opportunities to improve data workflows, automation, and digital systems

• Bachelor’s degree in a quantitative field (e.g., Public Health, Epidemiology, Data Science, Statistics) or related discipline.

• Master’s degree preferred (e.g., MPH, MSc in Biostatistics, or similar), especially with coursework in research methods and advanced analytics.

• 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).

• 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.

• 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.

• Working knowledge of languages, other than English, is a plus, particularly French and Spanish.

• Proficiency in R and/or Python.

• 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.

• Demonstrated ability to build and deploy digital data collection forms using ODK (XLSForm) is required.

• Delivering accurate, timely, and high-quality data analyses and reporting.

• Developing automated data workflows, dashboards, and digital data collection tools that improve program efficiency.

• Supporting field teams through effective technical assistance and capacity building.

• Collaborating successfully with cross-functional teams to strengthen MEL systems and program performance.

• Continuously improving technical skills and contributing to innovative MEL solutions.