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MEAL Officer (Data Analytics and Insights) – Maternity Cover
About us: War Child Canada (WCC) is an internationally recognized charity working with children and communities affected by war. In Uganda, WCC delivers programmes spanning child protection, education, and youth economic empowerment, with a growing commitment to innovation and data-driven programme delivery. WCC Uganda is at a pivotal moment in integrating data science, predictive analytics and digital tracking into its field operations to deepen impact and accountability.
Our Vision: is “Accelerating Peace By Disrupting The Cycle Of Violence”
Our Mission: is “Driving Generational Change For The Hardest Hit By Investing In The Power Of Local Communities”
To fulfil this, WCC is seeking for self-motivated, qualified individual to fill the role below:
Position Title: MEAL Officer (Data Analytics and Insights)
Contract Terms: Short-Term (Maternity Cover) -approx. 4 months
Start date: On or around 1 July 2026
Duty Station: Kampala (1 month), Yumbe Field Office (3 months)-West Nile (frequent field travel required)
Reporting to: Country MEAL Manager (primary) | Head of Programs (secondary)
Working Relationship:
GFS and School Feeding Project Managers and programme teams;
Implementing partner M&E focal points;
HQ MEAL Officer (technical guidance and quality assurance);
Regional Coordinator (as required).
Projects: Geared for Success (GFS) | School Feeding Pilot
Position summary: The MEAL Officer (Data Analytics and Insights) will provide dedicated MEAL coverage across two active projects in West Nile: the Geared for Success (GFS) project with support from Global Affairs Canada (GAC), which is in its last 2years of implementation, and the School Feeding Pilot Project, which is in an active data-collection and learning phase. The MEAL Officer will be based at the Kampala Office (1 month) and subsequently Yumbe Field Office (3 months) for approximately 4 months working days commencing July 2026.
In its School Feeding Pilot, WCC Uganda is integrating advanced data analytics and AI including Machine Learning (ML), Natural Language Processing (NLP) and Digital Smart Card-based participant tracking into its programme operations building a new generation of field intelligence systems that move beyond descriptive reporting into diagnostic, predictive and prescriptive decision-making for children and communities. The successful Candidate will have both the MEAL grounding and the technical aptitude to advance this agenda at field level. The role bridges rigorous field-level accountability with evidence-based innovation turning programme data into strategic intelligence that informs decisions and improves outcomes for children.
Scope of Work and Key Responsibilities
Indicator Tracking and PMF Management.
Update and maintain the Project Monitoring Framework (PMF) with monthly progress data against all output and outcome indicators.
Flag underperformance trends and support programme teams in developing corrective action plans.
Ensure indicator definitions, baselines and targets are consistently applied across all reporting.
Digital Participant Tracking and Data Systems.
Manage and maintain the Digital Smart/QR Card-based learner attendance and meal distribution tracking system across project schools.
Ensure ActivityInfo databases are continuously updated and accurately reflect field data for all GFS and School Feeding indicators.
Support implementing partner data submissions, including verification and quality checks prior to upload.
Troubleshoot data entry issues and provide on-the-job guidance to field staff and partners
Data Analytics and Insights.
Develop and apply analytical models to identify trends in learner attendance, dropout risk, feeding consistency and programme performance.
Build and maintain real-time dashboards (using Power BI, Tableau or Python/Streamlit) providing live visibility into programme delivery for management and donor reporting.
Translate analytical outputs into concrete, actionable recommendations for programme teams, including targeting, resource reallocation and adaptive planning decisions.
Explore predictive and prescriptive analytics approaches to early identification of at-risk learners and underperforming implementation sites and recommend actions to key stakeholders.
Ensure all learner and community data complies with War Child Canada’s data protection policies and Uganda’s Data Protection and Privacy Act 2019.
Apply ethical frameworks to ML model development including bias auditing ensuring predictive tools do not disadvantage girls, children with disabilities, or other marginalized learners.
Post Distribution Monitoring (PDM) – GFS.
Plan, coordinate and lead PDM exercises for scholastic materials, dignity kits and farm inputs.
Analyse PDM findings and produce reports with clear, actionable recommendations for programme teams.
Ensure use of standardised PDM tools and timely deployment aligned with distribution schedules.
School Feeding Monitoring.
Track daily feeding implementation including meals served per school, consistency against planned schedules and supplier performance.
Support data collection and analysis of attendance records before and during feeding cycles.
Track and compare enrolment, attendance and retention rates across intervention schools.
Document emerging outcomes, operational challenges and lessons learned.
Data Quality Assurance (DQA).
Conduct monthly data verification spot checks across all data sources and systems.
Lead at least one formal DQA exercise per quarter in line with WCC and donor standards.
Identify and resolve discrepancies between source documents, ActivityInfo records and programme reports.
Reporting and Documentation.
Compile and submit monthly internal monitoring reports by the 3rd of each following month.
Prepare quarterly review reports and contribute substantive inputs to the semi-annual donor report due October 2026.
Document success stories, outcome-level changes and lessons from gender- and conflict-sensitive interventions.
Ensure all reports are evidence-based, aligned with PMF indicators and supported by verified data.
Community Accountability and Learning.
Support community feedback and accountability mechanisms across project sites.
Explore and pilot multilingual NLP approaches for processing community feedback collected in Lugbara, Aringa, Madi and other West Nile languages, positioning WCC Uganda as a frontier innovator in community accountability within the humanitarian sector.
Facilitate project learning reviews and document lessons to inform adaptive programming.
Participate in internal project review meetings and external M&E coordination forums as required.
Handover and Knowledge Transfer.
Maintain comprehensive documentation of all systems, tools, processes and analytical workflows throughout the assignment, using clear version control practices.
Maintain a dedicated GitHub repository for all code, scripts, dashboards and automation tools developed during the assignment with clean, commented code, a README guide, and commit history ensuring full reproducibility and continuity beyond the consultancy period.
Produce a Technical Handover Pack at the end of the assignment comprising: fully updated data files, system documentation, model methodology notes, dashboard access credentials, and a step-by-step operational guide for the substantive post-holder.
Conduct a structured handover session with the Country MEAL Manager and incoming/returning post-holder, walking through all live systems, pipelines and pending tasks.
Ensure all data assets, code and documentation are stored in WCC Uganda’s designated organisational repositories in compliance with WCC’s data governance and protection policies.
Required Qualifications and Experience:
Essential Education
Bachelor’s degree in data science, Statistics, Computer Science, Mathematics or a closely related quantitative field
Desirable Education
Postgraduate diploma/degree or certification in Machine Learning, NLP or Artificial Intelligence
Essential Experience
3-4 years applying data science or analytical skills professionally with real-world datasets
Proven experience in data quality assurance and results-based indicator reporting
Desirable Experience
Prior experience in education, food security or livelihood programmes in humanitarian contexts
Familiarity with West Nile or refugee-hosting districts in Uganda
Essential Technical Skills
Python or R (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)
Data visualisation: Power BI, Tableau, Plotly, Streamlit
SQL; survey platforms (ActivityInfo, KoBoToolbox, ODK)
Desirable Technical Skills
Advanced ML: XGBoost, LightGBM, survival analysis
NLP: spaCy, NLTK, HuggingFace (incl. multilingual)
GIS: QGIS, GeoPandas; API integrations; QR/digital tracking
Essential Sector Knowledge
Strong analytical and report-writing skills in English
Data quality assurance and indicator tracking experience
Desirable Sector Knowledge
GAC, Master Card Foundation, WFP or UNICEF donor reporting frameworks
Conflict-sensitive and gender-responsive M&E approaches
Working Style
Ability to work independently under tight timelines
Willingness to travel frequently including field deployment to Yumbe or Adjumani
How to apply
Interested candidates should submit the following to: [email protected]
A cover letter (maximum 1 page) addressing: (1) what draws you to this specific role; (2) one analytical/AI project you are proud of and why; and (3) how you think data and analytics can improve outcomes for children in Uganda; and a current CV (maximum 3 pages) highlighting relevant technical skills, tools and experience, in a single PDF document
Contact details of two professional referees.
Eligibility: The position is open to only Nationals
War Child Canada is committed to providing a work environment in which all individuals are treated with respect and dignity. Final candidates will be vetted in accordance with War Child Canada’s Child Safeguarding Policy, including appropriate reference and security checks.