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: You will part of a small team assisting the business with statistical analysis and building predictive models for insurance pricing, underwriting and fraud risk.
About the Role: We are looking for a 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 degree in computer science, mathematics, statistics or quantitative methods (or equivalent experience). Master's Degree
- 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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