Evidence Action's mission is to fill the gap between what is effective in global health and development and what is implemented at scale. One of our flagship programs is the Deworm the World Initiative, which envisions a world where all at-risk children have improved health, increased access to education and better livelihoods potential as a result of being free of intestinal worms.
We are recruiting to fill the position below:
Job Title: Data Analysis Coordinator, MMS & SQ-LNS (Consultant)
Location: Oyo
About The Role
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.
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 outputs (performance report, dashboard update and priority-facility list) follow within 7 working days
Prepared meetings on time. State and LGA review-meeting data packs for MMS and SQ-LNS are ready at least 3 working days before each meeting
Kept data quality high. Every dataset passes documented quality checks before analysis. Recurring data quality issues are flagged to Field Operations and program teams within one week, with a recommended fix
Produced reproducible work. All cleaning and analysis is scripted (Stata or R), version-controlled and documented, so that any output can be re-run and checked by another analyst
Produced results that are used. Findings are clearly linked to recommended actions, and program teams and government counterparts use them in review meetings, action plans and donor reporting.
Requirements Education:
Bachelor's Degree in Statistics, Economics, Data Science, Public Health, Epidemiology or another quantitative field
A Master's degree in a quantitative field is an added advantage.
Experience:
At least 5 years of experience in data management and analysis for health or nutrition programs, ideally in routine monitoring of facility-based or community-based programs in Nigeria
A proven record of delivering accurate analysis under tight deadlines while managing several data streams at the same time.
Technical Skills:
Strong, hands-on proficiency in Stata and/or R for data cleaning and analysis, with a track record of writing reproducible, well-documented scripts
Experience with mobile data collection platforms, preferably SurveyCTO (or ODK/KoBo), including form programming and high-frequency checks
Sound knowledge of descriptive and basic inferential analysis
Advanced Excel skills
Experience building dashboards and visualizations (such as Power BI, R Shiny or Tableau)
Ability to use AI tools and automation to speed up routine data cleaning, checking and reporting.
Communication:
Strong written and verbal communication skills, including the ability to explain results clearly to non-technical program and government audiences.
Personal attributes:
Strong attention to detail and a passion for working with data, with the ability to tell a clear story from it
A bias to action and a sense of urgency, so that results arrive in time to inform decisions
Self-directed, able to balance requests from two program teams while keeping to agreed deadlines and priorities
Intellectually curious and service-oriented, able to document and explain complex work clearly
Quick to learn and willing to form and adjust opinions based on evidence
A strong commitment to evidence-based practice and policy in the development field.
Other Requirements:
Legal authorization to work in Nigeria
Willingness to travel to program states (up to 20%), subject to security guidance
Familiarity with Nigerian health information systems and registers (such as DHIS2, ANC and GMP registers, LMIS)
Experience with maternal and child nutrition data (such as supplementation coverage and adherence, anthropometry)
Experience with mapping tools (QGIS, ArcGIS or R).