Opinion Research / Survey Data Analyst Consultant - Our Common Home

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 validat