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

Evidence Action · Oyo

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About Evidence Action

Evidence Action is a global nonprofit organization with an approach distinctive in international development—we exclusively scale interventions that are backed by strong evidence and can be delivered with exceptional cost-effectiveness. Since our founding in 2013, we have reached over 515 million people globally with our interventions. We deliver high impact for every dollar spent, as recognized by,, and.

We do development differently. We take a data-driven approach to identifying, scaling, and continuously improving programs that deliver immense impact, ensuring these solutions measurably improve the lives of millions. Our has helped governments in Africa and Asia deliver over 1.8 billion childhood deworming treatments, improving health, education, and incomes. provides sustained safe water access to over 10 million people in Africa, preventing childhood illness and deaths. 

We drive innovation: Our builds the next generation of exceptional programs, taking an approach most similar to a venture capital model. We are ruthless in that do not meet our criteria; those that advance deliver outsized impact for millions of people. 

We are global. Across nine countries in Africa and Asia, our 700+ people are united by a tremendous passion for our mission and impact. We are strong because of our diversity of experience, geography, and background—with over 90% of our people in the countries where our impact is focused. 

We have big ambitions. Over the next decade, we’re setting our sights higher: We aim to reach over 600 million people globally to fulfill our vision of a world where hundreds of millions of people in the poorest places have better opportunities and their lives are measurably improved. We believe in bold and urgent action—because poverty doesn’t wait, and neither should we. 

We need great people. Our success to date is due to the collective work of our amazing staff. To achieve our ambitious goals, we are looking for people who share our passion for evidence, cost-effectiveness, and scale. We seek individuals who take an entrepreneurial approach, enjoy tackling hard problems, and have a bias to action and results. If our vision and approach excite you as much as they do us, we invite you to join us.

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

1. 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.

2. 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

3. 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.

4. 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.

5. 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.

6. 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.

7. 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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

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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).

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