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Team Tagline
About the role
Evidence Action is a non-profit scaling proven, sustainable programs to benefit millions. It supports the Government of Nigeria in delivering two high-impact maternal and child nutrition programs: Multiple Micronutrient Supplementation (MMS) and Small-Quantity Lipid-based Nutrient Supplements (SQ-LNS). The Data Analysis Coordinator will own the end-to-end monitoring data pipeline for both programs, sitting within the Monitoring, Learning and Evaluation (MLE) team for West and Central Africa, reporting to the MLE Manager.
Required Skills
Preferred Skills
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Responsibilities
- Clean and analyze MMS monitoring data (FCT, Oyo, Jigawa) including baseline ANC register extraction, sentinel facility transition, spot-checks and bottle-weighing observations
- Clean and analyze quarterly SQ-LNS health facility monitoring data across supported states
- Calculate key indicators by state, LGA and facility (ANC attendance, MMS coverage, stock, coverage of eligible children, stockout history, retention, staff knowledge, distribution challenges)
- Run descriptive and basic inferential analysis (proportions with confidence intervals, cross-tabs, trends, subgroup comparisons, simple regression) and interpret results for program action
- Support MMS coverage survey with processing, quality checks and survey-weighted estimates
- Support SQ-LNS endline household survey by monitoring high-frequency checks, reviewing anthropometric data quality and providing analytical support
- Produce high-frequency check outputs, cleaned datasets, topline summaries and full analysis outputs within turnaround times
- Prepare state and LGA review-meeting data packs and quarterly performance reports with priority-facility lists
- Build dashboards, maps and visualizations; write plain-language summaries and slides
- Contribute data inputs to donor reports, memos and learning products; flag anomalies to MLE Manager and program teams
- Build and test SurveyCTO forms with skip logic, constraints and range checks; support enumerator training
- Set up automated high-frequency checks for duplicates, outliers, survey duration, GPS, submission patterns and enumerator performance
- Apply documented quality checks; flag recurring data quality issues with recommended fixes
- Develop and maintain scripted, version-controlled cleaning and analysis pipelines in Stata or R
- Maintain codebooks, indicator definitions, cleaning logs and analysis documentation
- Respond to ad hoc data requests from program teams and prepare state/LGA performance data for planning and review
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