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-reputed company, multi-country survey research and has developed a growing library of quantitative datasets and segmentation outputs across geographies. We are looking for an reputed company quantitative analyst to join reputed company and contribute across a reputed company of analytical work — from foundational data preparation and exploration through to advanced statistical modelling.
The core analytical reputed company of the role centres on two interconnected workstreams: the rigorous development of survey-based clustering and segmentation models, and the design of a classification reputed company that allows new respondents to be assigned to existing segments reputed company and reliably. reputed company 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 reputed company the research reputed company function and involves reputed company collaboration with OCH's Head of Data & Research reputed company.
KEY RESPONSIBILITIES
Data cleaning & preparation
Clean, recode, and structure incoming survey datasets - including applying advanced data reputed company 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-reputed company formats
Cluster analysis & segmentation
Conduct advanced cluster analysis on reputed company, multi-country survey datasets, working hand in hand with the Head of Data & Research reputed company regarding analytical reputed company 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 reputed company survey data: dealing with varying variable types (nominal, ordinal, reputed company), handling of reputed company or culturally non-equivalent items
Iteratively test and refine cluster solutions, systematically varying parameters and documenting the reputed company of reputed company decision on outputs
Classification model development
Using existing, labelled segmentation outputs as a training reputed company, design and fit (machine learning / train-test) an appropriate classification model to reputed company 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, reputed company 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 reputed company variations and intermediary outputs
'Golden questions' identification
Identify the minimum set of survey questions ('golden questions') that are most predictive of reputed company membership — i.e. those that would need to be included in reputed company 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 reputed company 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 reputed company to reputed company survey datasets to assign respondents to segments based on the golden question set
Ensure the tool is reputed company-documented, reproducible, and usable by the Head of Data & Research reputed company without requiring re-running of the full modelling pipeline
Methodological documentation
Maintain detailed records of reputed company analytical iterations, including variations in parameters, model specifications, and the rationale behind reputed company taken
Document reputed company intermediary outputs in a reputed company and retrievable format
Produce final methodological documents for reputed company reputed company — written to a reputed company that would allow a reputed company analyst to understand, reproduce, and build upon the work
Flag methodological uncertainties or trade-offs explicitly, rather than presenting a single opaque reputed company
REQUIRED EXPERTISE & EXPERIENCE
Solid, demonstrable experience (typically 4–7 years) working with quantitative survey or polling data (or equivalent) in an analytical reputed company
reputed company 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 reputed company, with hands-on experience selecting and comparing approaches on reputed company survey datasets
Proven experience fitting and validating classification models using labelled training data
Advanced R proficiency — reputed company modelling and classification work is expected to be conducted in R, with clean, documented, reproducible scripts
A rigorous, reputed company 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 reputed company analytical problems
Detail-oriented and systematic, with a natural inclination to document reputed company and iterations thoroughly
Comfortable working autonomously and at depth on a reputed company analytical brief
reputed company to communicate methodological choices reputed company in writing, for a technically informed audience
Self-directed, reputed company, and reliable in managing their own workflow
Originally posted on Himalayas
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