Data Scientist - Insurance, Data Science (R, Python, SAS, SQL), Predictive Analytics, Multivariate Procedures, Statistics, Data Mining, Datamodelling, Machine Learning, RPA, ETL, Text Mining, Business Intelligence. Permanent, London. £70k - £80k+ (Fully Negotiable on Experience) +Bonus & Benefits - Hybrid

Data Scientist required to work with a Global Insurance company. Working in a hands-on capacity from a Data Programming, Statistical/Datamodelling capacity you will work within a team comprised of 3 Data Scientists and have responsibility for helping insurance product teams understand their data and drive output across multiple products

Working with large data sets across multiple sources from within the business and its subsidiaries you will develop advanced data analysis techniques, customised modelling solutions and leverage Big Data from a data science perspective and feed these analytical capabilities into the ongoing development of systems for the business and its clientele worldwide. The Data Scientist will build forecasts and models, provide insights, explore trends and implement data analytics tools for to communicate insight from new machine learning techniques to a wider team.

The successful Data Scientist will have a background in Data Analytics, Business Intelligence or Management Information Systems and will possess strong hands on experience with SQL. This will be combined with strong experience gained with contemporary Data Science languages R or Python. You will be experienced in databased design as well as data visualisation tools or Business Objects. Critically, you will have gained Practical knowledge in applying machine learning techniques and be familiar with both APIs and Data Scraping. Degree level education preferred; excellent communication skills pre-requisite.

Excellent opportunity to join a global organisation with an exceptional reach and exciting products/platforms to play a key role in the development of Data Science within the business. Very stable company, City of London base/location.