Data Scientist
Summary
Build and evaluate statistical and ML models for banks using Python and SQL, then hand them off for production while communicating findings clearly to decision-makers.
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.