About our Team: You will part of a small team assisting the business with statistical analysis and building predictive models for credit, fraud, and identity risk.
 

About the Role: We are looking for a Data Scientist to conduct statistical analysis and build predictive models for credit, fraud, and 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 credit and fraud risk statistical models to be used by existing and new customers

• 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, Technology and Marketing 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 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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