Collaborate closely with colleagues, clients and academics to resolve complex issues using causal inference methods on unique and complex datasets.
Conduct empirical and quantitative analysis in Excel, Stata, R and/or Python, with a focus on advanced methods in econometrics and machine learning.
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 computer science.
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 in machine learning and statistical programming.
Proficiency in using Stata, R or Python for data wrangling, causal analysis, hypothesis testing and data visualisation (essential).
Familiarity with, or interest in, learning other statistical programming languages (desirable).
Proficiency in Excel is a plus.
Strong ability to communicate empirical findings clearly through compelling visuals that simplify complex datasets or results.