Collaborate closely with colleagues, clients and academics to resolve complex issues using causal inference methods on unique and complex datasets.
Build and validate datasets, apply causal inference or other statistical techniques, test economic hypotheses and communicate your work through clear and impactful visuals.
Contribute to writing reports, presentations and articles to communicate research insights effectively to both technical and non-technical audiences.
Actively contribute to the Analytics and Data Science team.
Engage with others across teams to share expertise and continuously learn from peers.
Requirements
An excellent academic record, with an undergraduate or postgraduate degree in a quantitative field such as economics, emphasising statistics and/or econometrics for causal inference and hypothesis testing.
A PhD degree is a plus.
If a relevant degree is not available, a proven history of robust, reliable data analysis focusing on causal inference and hypothesis testing is essential.
Strong problem-solving abilities and analytical and communication skills.
Previous experience of producing insights and providing recommendations within a client-facing context is advantageous.
Experience with building simulations, agent-based models, continuous or discrete choice models, or time series models (advantageous).
Proficiency in using Stata, R or Python for data wrangling, causal analysis, hypothesis testing and data visualisation (essential).
Familiarity or interest in learning other statistical programming languages is desirable.
Proficiency in Excel is a plus.
Strong ability to communicate empirical findings clearly through compelling visuals that simplify complex datasets or results.