Master’s degree in Economics, Development Studies, Public Policy, Statistics, Data Science, Social Sciences, or a related field is required.
A minimum of 5 to 7 years of relevant professional experience in social protection, poverty reduction, international development, or a closely related field, with demonstrated exposure to national or federal-level programme contexts.
Strong knowledge of social protection systems and programmes, including familiarity with conditional cash transfers, graduation models, and the institutional architecture of social protection delivery in Nigeria or comparable settings.
Proficiency in data analysis and visualization tools, including advanced Microsoft Excel, at least one statistical software package (Stata, R, or Python), and data visualization platforms such as Power BI or Tableau.

Stakeholder Engagement and Coordination

Position the lab as a credible, trusted technical partner supporting coordination and evidence-informed decision-making within the federal social protection ecosystem.
Facilitate the development of inter-agency data sharing and coordination frameworks, and formalize institutional arrangements that improve the flow of programme data across federal and state levels.

Represent IPA at federal-level policy forums, technical working groups, inter-ministerial coordination meetings, and external engagements, communicating the lab’s work and positioning IPA as a thought leader in social protection evidence and policy in Nigeria.

Evidence Generation and Research

Lead the development of the embedded lab’s framework and operating model, including its mandate, objectives, governance structure, defined roles and responsibilities, decision-making processes, workflows, and engagement mechanisms with government partners.
Develop and implement standard operating procedures (SOPs) for core lab functions such as data mapping, evidence intake, data analysis and reporting, research prioritisation, and learning cycles.

Design and manage primary data collection activities and administrative data reviews, ensuring methodological rigor and direct alignment with the institution’s information and decision-making needs.

Lab Management and MEL Systems Strengthening

Develop and implement a MEL framework for the lab’s operations, including a work plan, results framework, and reporting protocols that enable transparent tracking of lab activities and outcomes against agreed objectives.
Support the partner institution in strengthening its central data infrastructure, working with technical staff to operationalize existing data systems, automate data flows between programme units and central repositories, in line with data governance protocols.

Co-create standard operating procedures, guidelines, and quality assurance protocols for sustainable data management within the institution.

Data Analytics and Reporting

Lead hands-on quantitative and qualitative analysis of social protection programme data, translating findings into clear and decision-relevant insights.
Design and build data dashboards, scorecards, and visualization tools that enable institutional leadership and programme managers to monitor performance against key indicators, using platforms such as Power BI, Tableau, or Excel-based solutions appropriate to the institution’s infrastructure.

Develop and institutionalize reproducible analytical workflows, documentation standards, and data systems that can be independently maintained, updated, and scaled over time.

Capacity Building and Technical Support

Design and deliver targeted capacity building interventions for institutional staff on social protection MEL, data analysis, evidence interpretation, and evidence-to-policy translation, adapting content and delivery modalities to different staff levels and learning needs.
Provide ongoing on-the-job coaching and technical mentoring to M&E officers, programme managers, and data analysts, building their ability to independently generate and use evidence for programme adaptation and policy decisions.

Facilitate structured learning events including data review meetings, evidence-to-action workshops, and cross-programme learning sessions that promote a culture of continuous learning and evidence-informed adaptation within the institution.

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