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 and engage with others across teams to share expertise and continuously learn from peers.
Provide client-facing analysis and insights, with exposure from day one.
Requirements
An excellent academic record, with an undergraduate or postgraduate degree in a quantitative field such as economics, emphasising statistics and/or computer science.
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.
Essential: proficiency in using Stata, R or Python for data wrangling, causal analysis, hypothesis testing and data visualisation.
Familiarity or interest in learning other statistical programming languages is desirable.
Proficiency in Excel is a plus.
Eligibility notes: check FAQ for important information about eligibility and the application process.
Application rule: applications that use AI to generate a cover letter will be rejected; use of AI during interviews or written exams will result in rejection.