Data Scientist
Summary
Build statistical and machine-learning models to extract insights from banking data and guide financial decisions, then hand them off for production.
Analyse complex enterprise data, find the patterns that matter and build the statistical models behind decisions banks actually act on.
What you’ll do
- Frame business problems as analytical ones, and be honest when the data cannot answer the question asked.
- Build, evaluate and iterate statistical and machine-learning models against real client data.
- Work directly with domain experts in banking, insurance and investment management to validate what the numbers mean.
- Communicate findings to people who will make decisions on them — clearly, with the uncertainty stated.
- Hand models to ML engineers in a state they can productionise, with evaluation and assumptions documented.
What we’re looking for
- Strong grounding in statistics, experimental design and the limits of both.
- Fluency in Python and SQL; comfort with the modern analysis stack.
- Experience taking an analysis from question to defensible conclusion, not just to a notebook.
- The judgement to say when a simpler model is the right answer.
- Financial-services or other regulated-domain experience is an advantage, not a requirement.