Data Analysis Coordinator, MMS & SQ-LNS (Consultant) - Abuja

Evidence Action

Data Analysis Coordinator, MMS & SQ-LNS (Consultant) - Abuja

Posted: Oct 23, 2026 Deadline: Not specified

Evidence Action launched formally in 2013 to scale programs with sustainable business models that have been proven to be effective so that they benefit millions of people.

Evidence Action leads and manages two programs incubated by Innovations for Poverty Action: Dispensers for Safe Water and the Deworm the World Initiative. We also run Evidence Action B...

Data Analysis Coordinator, MMS & SQ-LNS (Consultant) - Abuja

• Job Type Contract
• Qualification BA/BSc/HND
• Experience 5 years
• Location Abuja
• Job Field Data, Business Analysis and AI

About The Role

• Evidence Action supports the Government of Nigeria to deliver two high-impact maternal and child nutrition programs. One is Multiple Micronutrient Supplementation (MMS) for pregnant women attending antenatal care. The other is Small-Quantity Lipid-based Nutrient Supplements (SQ-LNS) for children aged 6 to 18 months. Both programs depend on fast, reliable monitoring data so that program teams and government counterparts can find and fix implementation problems while there is still time to act.
• You will own the end-to-end monitoring data pipeline for both programs. That means building and running SurveyCTO forms and high-frequency checks, cleaning and analyzing field data, and delivering decision-ready outputs within agreed turnaround times: review-meeting packs, performance reports, dashboards and priority-facility lists. Speed with accuracy is the defining expectation of this role.
• You will sit in the Data Analysis team within Evidence Action's Monitoring, Learning and Evaluation (MLE) team for West and Central Africa. You will work closely with the MLE Field Operations team, which collects the data, and with the MMS and SQ-LNS program teams, who use the results. You will report to the MLE Manager and will have no direct reports.

Key Responsibilities

• Deliver monitoring data analysis for MMS and SQ-LNS
• Clean and analyze each round of MMS monitoring data (FCT, Oyo and Jigawa), including baseline ANC register extraction, the transition of baseline facilities to sentinel facilities, recurring spot-check visits and bottle-weighing adherence observations
• Clean and analyze each quarterly round of SQ-LNS health facility monitoring data across Evidence Action-supported states
• Calculate key indicators by state, LGA and facility, including ANC attendance, MMS coverage and continuity, product in stock at the time of visit, coverage of eligible children per GMP register, stockout history, retention through the 6-month enrolment, staff knowledge of eligibility and dosing, and distribution challenges
• Run descriptive and basic inferential analysis, including proportions with confidence intervals, cross-tabulations, trend and subgroup comparisons, and simple regression where appropriate. Interpret what the results mean for program action
• Support the MMS coverage survey with processing, quality checks and survey-weighted estimates
• Support the SQ-LNS endline household survey (planned for early 2027) by monitoring survey-firm high-frequency checks, reviewing anthropometric data quality (plausibility flags, digit preference, z-score distributions) and providing analytical support to the evaluation team
• Produce decision-ready outputs
• Produce high-frequency check outputs, cleaned datasets, topline summaries and full analysis outputs for each data round, within the turnaround times set out above
• Prepare state and LGA review-meeting data packs
• Produce quarterly performance reports and priority-facility lists that flag facilities and supply chain gaps needing targeted follow-up
• Build dashboards, visualizations and plain-language reporting
• Build and update dashboards, maps of facility performance and simple visualizations for program and government audiences
• Write short, plain-language summaries and slides that lead with the key finding and the recommended action
• Contribute data inputs to donor reports, memos and learning products
• Flag unexpected patterns, anomalies or emerging trends to the MLE Manager and program teams as soon as they are identified
• Design and manage digital data collection tools and high-frequency checks
• Build and test SurveyCTO forms for monitoring tools (such as spot checks, register extraction and facility monitoring), including skip logic, constraints and range checks
• Support enumerator training on digital tools
• Set up and run automated high-frequency checks for duplicates, outliers, survey duration, GPS, submission patterns and enumerator performance
• Share daily feedback with the Field Operations team so issues are fixed while teams are still in the field
• Maintain data quality and reproducible workflows
• Apply documented quality checks to every dataset before analysis
• Flag recurring data quality issues to Field Operations and program teams within one week, with a recommended fix
• Develop and maintain scripted, version-controlled cleaning and analysis pipelines in Stata or R, so any output can be re-run and checked by another analyst and later rounds are turned around faster than the first
• Maintain codebooks, indicator definitions, cleaning logs and analysis documentation for each data round
• Support program teams and government counterparts
• Respond to ad hoc data requests from the MMS and SQ-LNS program teams within agreed timelines
• Prepare state-level and LGA-level performance data for planning, review and health worker meetings
• Explain results clearly to non-technical audiences and link findings to recommended actions
• Manage data securely and document processes for handover
• Organize and store raw and clean data files on approved platforms, such as Box, following Evidence Action data management and data protection protocols, including the secure handling of personally identifiable information
• Document lessons learned and process improvements so data systems can be handed over cleanly at the end of the contract

Work expectations

• Location: Abuja, Nigeria (preferred). Candidates based in Oyo or Jigawa State will also be considered
• Travel: Up to 20% to program states, subject to Evidence Action security protocols
• Reporting line: MLE Data Manager. No direct reports

Key Role Expectations

By the end of the contract, you will be expected to have:

• Delivered fast turnaround on every data round. Daily high-frequency check outputs are shared within 24 hours during active data collection. A cleaned dataset and a topline summary are shared within 5 working days of the end of each data collection round. Full analysis ou