Data Scientist
Role details
Job location
Tech stack
Requirements
A strong quantitative background in data science, statistics, economics, behavioural science, psychology, or a related social science.
Professional experience applying data science or quantitative analysis in real-world delivery contexts, such as policy analysis, impact evaluation, consulting, or applied research.
Strong applied data science capability, including:
working with messy, real-world datasets,
exploratory data analysis and feature construction,
statistical modelling and inference,
and development of reproducible analytical workflows.
Proficiency in Python for data analysis.
Experience contributing to, or working closely with, impact evaluations or applied causal analysis, with an understanding of counterfactual reasoning and common threats to validity - including:
experience with quasiexperimental or causal methods such as differenceindifferences, regressionbased designs, matching, or related approaches; and
applied experience drawing on behavioural or social science to inform evaluation design, analysis, or interpretation.
Ability to critically assess analytical outputs - including results produced by automated or AI-assisted approaches - and to explain their strengths, limitations, and assumptions clearly.
Strong written and verbal communication skills, including the ability to communicate technical and methodological concepts to non-technical audiences.
Desirable (but not essential)
Experience contributing to the development, testing, or use of AIenabled analytic tools or workflows, including decisionsupport or semiautomated analysis.
Experience working in consulting, government, or public sector research environments.
Experience contributing to technical sections of reports or proposals for clients.