Data & Research Analyst at Lafiya Nigeria

Position Overview

• As Lafiya transitions from a startup to a national-scale organisation, we want to rigorously prove our impact.
• We are looking for a technically-driven data analyst to lead the quantitative backbone of our Monitoring, Evaluation & Learning (MEL) department, from the routine analysis of our counselling data to the statistical analysis behind our evaluations.
• You will turn both our day-to-day programme data and our formal research into the defensible evidence needed to influence national policy and secure government adoption.
• Day-to-day, you will own the ongoing quantitative analysis of our routine counselling data that informs internal decisions in near real time.
• Alongside this, you will lead the statistical analysis for our evaluations, designing sampling and analysis strategies and applying rigorous methods like difference-in-differences modelling, so our evidence meets the standards of international donors and academic partners.
• Evaluation implementation (e.g. managing enumerators or field data collection) may occasionally be part of the role, but it is not the focus.
• You'll work closely with our Strategy & Impact Analyst, who translates evaluation findings and programme data into cost-effectiveness and decision models.
• Your analysis is a key input into that work, and their work will help sharpen what questions your evaluations should answer.
• This is a role for someone who is as comfortable with the routine, sometimes messy work of a live data pipeline as with the deep, abstract work of statistical modelling.
• If you want to take ownership of both the numbers behind our day-to-day programme and the evaluations and research that shapes its future, in a fast-moving, agile environment, this is the role for you.

What You’ll Do

• Counselling Data Analysis: own the ongoing quantitative analysis of routine programme counselling data (SurveyCTO, DHIS2, NHLMIS), building and maintaining the indicator calculations that feed our dashboards and reporting.
• Data Reconciliation & Quality: reconcile discrepancies across data systems, tracing figures back to their source and flagging gaps explicitly rather than smoothing over them, so the numbers the organisation relies on month to month are trustworthy.
• Trend & Cohort Analysis: produce regular trend, cohort, and continuation-rate analysis from routine and historical data, surfacing patterns that inform programme and strategic decisions in near real time.
• Evaluation Design & Advanced Statistical Analysis: design sampling and analysis strategies for our evaluations, and lead the statistical work behind them, from protocol design through to final analysis, including Difference-in-Difference (DiD) models and longitudinal tracking (e.g. writing the R scripts to match client IDs across historical datasets and track continuation rates).
• Publication-Ready Evidence: produce analysis of sufficient rigour for academic publication and high-level donor reporting (e.g., the Gates Foundation or GiveWell), translating statistical and evaluation findings into clear, defensible evidence.
• Technical Input to Stakeholder Conversations: contribute technical input when evaluation results are presented to Federal-level stakeholders and technical partners, helping ensure our evidence aligns with national health priorities.
• Support with evaluation field implementation: Occasionally, and depending on experience, follow up on evaluations such as enumerator training and field data quality oversight.

Who You Are

• We are looking for a rigorous thinker who is comfortable balancing methodological integrity with the operational realities of a fast-moving non-profit, being proactive, and determined to find the truth within the data.

You’re someone who:

• Is technically fluent and analytically sharp. You're comfortable working independently with large, messy datasets and writing code to clean, merge, and analyse them.
• You don't just run analyses; you explore patterns, test robustness, and challenge your own findings.
• Communicates clearly. You can explain confidence intervals and regression coefficients to non-technical stakeholders without losing meaning, and know how to speak to field teams, decision-makers, and funders effectively.
• Thinks theoretically, acts practically. You enjoy causal inference, study design, and statistical modelling, but you also know how to make them work in the real world.
• You can move from a theoretical research question to a feasible field evaluation, or from a statistical concept to a reliable indicator that can be tracked month after month.
• Can manage a research process. You might have experience conducting in-field experiments or evaluations in development contexts, and know how to train enumerators to minimise bias and manage data quality in real time.
• Is driven by learning. You are not afraid to ask questions or challenge assumptions. You see evaluation and data as tools for organisational improvement, not compliance exercises.
• You're comfortable saying when you don't know something, and you enjoy figuring it out.
• Is a rigorous truth-seeker. You care deeply about methodological integrity and credibility.
• You question assumptions, interrogate your own work, and are transparent about uncertainty and limitations; you believe strong evidence is built on honesty, not perfect results.
• Works autonomously. You don't wait to be told what to do next.
• You take ownership of your workstream, identify what needs attention, and move it forward without close supervision.

Education and Experience

• Bachelor’s Degree in Econometrics, Statistics, Data Science, Public Health, Economics, or a related field is mandatory. A Master's Degree is a plus.
• Experience in designing sampling strategies for evaluations is mandatory.
• Experience writing code for data cleaning and analysis (R, Python, Stata) is mandatory.
• Experience designing and managing quantitative surveys using SurveyCTO or Open Data Kit is preferred.
• Experience analysing routine monitoring or programme data (e.g. from DHIS2, LMIS, or similar MIS platforms), including reconciling data across multiple systems, is preferred
• Past experience managing research teams or enumerators in the field is a plus.

Skills & Competencies:

• Professional fluency in English is mandatory. Hausa is desired but not required.
• Proficiency in advanced statistical analysis, econometrics, and modelling using a statistical package (R, Python, Stata, SPSS, or SAS) is mandatory. We run a cloud RStudio setup, so familiarity with R in particular is a plus.
• Proficiency using spreadsheet software (Excel or Google Sheets) for analysis (e.g. VLOOKUP, pivot tables, data cleaning) is mandatory.
• Working knowledge of SQL for querying and working with data across our systems is a plus.
• Working knowledge of any Business Intelligence tool like Power BI, Tableau, Looker, Metabase, etc. for building analytical dashboards and reports is preferred.
• Comfort working with recurring, messy operational data pipelines, not only clean survey datasets
• You don't need to check every box here.
• The ‘mandatory' items are genuinely required, but ‘preferred’ and ‘a plus’ are bonuses, not bars to clear. If you meet the mandatories and this role excites you, please apply and don’t self-select out.

Why should you apply

• You will be joining a young, ambitious and supportive organisation that is growing quickly. We are serious about impact and serious about investing in our people.
• There will be room to take initiative, make decisions, and leave your mark on a critical stage of Lafiya’s growth. We don’t have layers of approval or slow processes – if something makes sense, we’ll try it.

Benefits include:

• 40 days of combined holiday and paid annual leave
• Fully paid maternity leave
• Flexibility in where, when and how you work
• Working closely with a team of senior leaders who are collaborative, ambitious and serious about impact.
• Other benefits, such as health insurance and office/co-working space(varies by location)
• Annual team retreat with all expenses paid