Survey Data Analyst – Consultant

🕒 April 23

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our common home

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

🤝 Non-profit

🌍 Social Impact

Non-profit • Social Impact

our common home is a non-profit organization that strengthens support for environmental protection by empowering local communities and civic institutions. It supports local initiatives, develops durable solutions aligned with citizens' priorities, builds leadership, and provides rigorous research, monitoring, and capacity-building to enable inclusive environmental action across countries. The organization partners with like-minded groups, emphasizes community-led solutions, and engages decision-makers to advance policies consistent with shared values.

📋 Description

• Data cleaning & preparation: Clean, recode, and structure incoming survey datasets - including applying advanced data quality checks and filters, raking & weighing, missing data, etc. • Conduct foundational data exploration including frequency distributions, cross-tabulations, and basic descriptive analyses, primarily in SPSS • Work fluently across survey data formats, principally SPSS (.sav) and R-native formats • Conduct advanced cluster analysis on complex, multi-country survey datasets, working hand in hand with the Head of Data & Research Methods regarding analytical decisions and final segmentation outputs • Evaluate and compare clustering approaches (e.g. k-means, hierarchical, latent class analysis, and others as appropriate) with a view to producing segments that are statistically robust, meaningful, and cross-nationally comparable • Manage the specific methodological challenges of complex survey data: dealing with varying variable types (nominal, ordinal, continuous), handling of translated or culturally non-equivalent items • Iteratively test and refine cluster solutions, systematically varying parameters and documenting the impact of each decision on outputs • Using existing, labelled segmentation outputs as a training base, design and fit (machine learning / train-test) an appropriate classification model to enable assignment of new respondents to established segments • Evaluate candidate classification approaches (e.g. random forest, logistic regression, LDA, gradient boosting, or others) and select the most appropriate given the data structure, segment separability, and intended use • Assess model performance rigorously using appropriate validation strategies (e.g. cross-validation, held-out test sets, confusion matrices, precision/recall) • Identify the minimum set of survey questions ('golden questions') that are most predictive of segment membership — i.e. those that would need to be included in future quantitative research instruments to allow reliable classification of new respondents • Apply appropriate variable importance and feature selection techniques to identify and rank candidate questions, and validate their predictive power • Produce clear recommendations on the golden question set, including supporting evidence and sensitivity analyses • Design and implement a practical classification tool or calculator that can be applied to future survey datasets to assign respondents to segments based on the golden question set • Maintain detailed records of all analytical iterations, including variations in parameters, model specifications, and the rationale behind decisions taken

🎯 Requirements

• Solid, demonstrable experience (typically 4–7 years) working with quantitative survey or polling data (or equivalent) in an analytical capacity • Fluency with SPSS for data cleaning, cross-tabulation, and exploratory data analysis, including confident management of variable and value labels, codebooks, and data transformations • Advanced proficiency in cluster analysis methods, with hands-on experience selecting and comparing approaches on real survey datasets • Proven experience fitting and validating classification models using labelled training data • Advanced R proficiency — all modelling and classification work is expected to be conducted in R, with clean, documented, reproducible scripts • A rigorous, structured approach to analytical work with a strong documentation habit.

🏖️ Benefits

• Fully Remote