Opinion Research / Survey Data Analyst (Consultant)

Opinion Research / Survey Data Analyst (Consultant) Time commitment: ~3–4 days per week, variable by phase. Duration: period tbc. Start-asap OCH's global research team conducts large-scale, multi-country survey research and has developed a growing library of quantitative datasets and segmentation outputs across geographies. We are looking for an experienced quantitative analyst to join the team and contribute across a range of analytical work — from foundational data preparation and exploration through to advanced statistical modelling. The core analytical focus of the role centres on two interconnected workstreams: the rigorous development of survey-based clustering and segmentation models, and the design of a classification framework that allows new respondents to be assigned to existing segments efficiently and reliably. Beyond this, the analyst will also handle day-to-day data management tasks including dataset cleaning, variable harmonisation, and exploratory cross-tabulation work. The role sits within the research methods function and involves close collaboration with OCH's Head of Data & Research Methods. KEY RESPONSIBILITIES 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 Cluster analysis & segmentation 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 Classification model development 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) Iterate on model specifications, documenting all variations and intermediary outputs 'Golden questions' identification 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 Classification / calculator tool 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 Ensure the tool is well-documented, reproducible, and usable by the Head of Data & Research Methods without requiring re-running of the full modelling pipeline Methodological documentation Maintain detailed records of all analytical iterations, including variations in parameters, model specifications, and the rationale behind decisions taken Document all intermediary outputs in a structured and retrievable format Produce final methodological documents for each workstream — written to a standard that would allow a qualified analyst to understand, reproduce, and build upon the work Flag methodological uncertainties or trade-offs explicitly, rather than presenting a single opaque output REQUIRED EXPERTISE & EXPERIENCE 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 KEY SKILLS & ATTRIBUTES Statistically rigorous and methodologically confident, with the seniority to take end-to-end ownership of complex analytical problems Detail-oriented and systematic, with a natural inclination to document decisions and iterations thoroughly Comfortable working autonomously and at depth on a focused analytical brief Able to communicate methodological choices clearly in writing, for a technically informed audience Self-directed, structured, and reliable in managing their own workflow Department Research Locations London, Oxford, Cardiff Remote status Fully Remote About Our Common Home our mission - building the common good for the environment. Protecting the environment should be a mainstream issue. We support the local networks that can make that happen. we increase support for protecting the environment, by empowering those who are currently left out of the conversation We do that by: Supporting local actors to take environmental action rooted in more mainstream values ​​and interests. Developing better, longer-lasting solutions that reflect the priorities of ordinary people. Creating the space for decision-makers from across the political spectrum to lead on environmental issues on their own terms. we respect and honor traditional values We share the mainstream values ​​​​of our partners. Our network is rooted in the communities where we work. we trust people to find a way Communities themselves are best placed to identify solutions to environmental issues that work for them. we are rigorous and accountable Our work is grounded in research of the highest standard. 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