About the Business: LexisNexis Risk Solutions is the essential partner for risk assessment. Within our Insurance business, we provide customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our solutions help drive better data-driven decisions across the insurance lifecycle, all while reducing risk and optimising processes. You can learn more about LexisNexis Risk at the link below. https://risk.lexisnexis.com/insurance
About our Team: The Data Science Rotational Programme will last 18 months and upon completion you will move full time into one of our core data science teams. The rotational aspect of the programme will allow you to work across various sub teams including motor, home and metal. The position is based out of our Dublin office. LexisNexis operate a hybrid work environment with the option to work from home 2-3 days a week.
About the Role: We are looking for a Graduate Data Scientist to conduct statistical analysis and build predictive models for insurance pricing, underwriting and fraud risk. The ideal candidate will have experience in data mining, statistical methods, and modelling / scoring techniques. They will balance day-to-day analytics assignments, research experiments and will contribute to the advancement of the global data science group.
Responsibilities
Building and testing insurance pricing, underwriting and fraud risk statistical models, consulting in support of existing and new customer sales
Providing complex analytical results in clear, simple messaging to evidence the value provided by our products
Following modelling best practices and provide feedback on ways to enhance current processes
Providing technical support and be a resource to internal partners in Product, Sales and Technology teams
Researching new technologies and bring forward new ideas to the group
Supporting and help to shape our data science strategy
Requirements
Have BSc. or MSc. degree in computer science, actuarial science, mathematics, statistics or quantitative methods (or equivalent experience).
Be able to demonstrable experience or knowledge of applied modelling and analytics experience in applicable industry
Have good understanding of statistical methods applied to data analysis
Have user experience of R, Python, SAS, SPSS or equivalent analytic software.
Have understanding of various statistical methodologies including linear regression, logistic regression, and other advanced analytic techniques
Have good written communication skills, including the ability to describe statistical results to non-statistical audiences.
Experience processing large data sets and matching/merging multiple data sets.
Learn more about the LexisNexis Risk team and how we work here
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