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About the job
Risk Modelling Analyst
£35,000 + Benefits
A hands on role for a technically strong analyst with a statistical modelling background (and ideally insurance knowledge). Youll be manipulating, analysing and modelling unleveraged data to optimise underwriting, pricing and fraud risk decisions. Its a business critical role with huge scope to have an impact on the business, digging deep into the data and models to provide insight and boost business performance.
A growing insurance company with a relaxed, but fast paced environment. Youll be encouraged to extend yourself and make the role your own and will be offered support and ownership. The perfect environment to grow as an analyst!
Create useful data enrichment/optimisation factors, enhancing the use of datasets and experimenting with techniques to leverage unused data
Develop statistical models to enhance Risk decisions using enriched data factors (using R, Python)
Identify and investigate potential new data sources and enrichment factors (using SAS, SQL) that could potentially be used to increase accuracy of risk predictions (underwriting, pricing and fraud)
Monitor practical use and accuracy of enrichment factors. Are they fit for live use? How do live results compare with expected results?
Work cross functionally using analytics and data science to provide insight and optimise underwriting, pricing, fraud, marketing..
YOUR SKILLS AND EXPERIENCE:
Educated to a degree level (minimum) in a numerate discipline
Knowledge of inurance or pricing models preferred (e.g. underwriting, pricing models or other risk models like scorecards)
Skilled in SAS, SQL, R, Python or similar
Experience developing statistical models or applying data science techniques such as regression, clustering, decision trees, GLM, classification
Ability to analyse and optimise performance of models
Work cross functionally with different teams across the business
Business critical role with genuine value add
SAS, SQL, R, Python, Insurance, Risk, Underwriting, Fraud, Price, Data Analysis