公司：LexisNexis 职位：Statistical Modeler
Basic Job Function
This position exists to conduct statistical analysis and build predictive models for credit risk, fraud, and collections in the modeling/scoring functional area. The incumbent will be technically proficient in modeling/scoring techniques and methodologies.
Accountabilities:
1. Develop scoring models and statistical analysis, provide SAS code to customers or internal contacts to validate accuracy of production scoring code.
2. Follow modeling guidelines and standards and provide feedback on ways to enhance current practices.
3. Provide technical support and be a resource to internal and external modelers.
4. Build statistical models and complete various analytical projects for marketing, underwriting, claims solutions and support internal product development projects.
5. Conduct ad-hoc analysis and performance reports in support of existing and new customer sales
Qualifications:
Bachelors degree in computer science, mathematics, statistics or quantitative methods (or equivalent years experience)5+ years Experience in building predictive models using SAS or similar software package.
1. Applied modeling and analytics experience in applicable industry.
2. User of SAS, SPSS or equivalent analytic software.
3. Understanding of various statistical methodologies including linear regression, logistic regression, neural networks, and CHAID/CART.
4. Fluency with Excel, PowerPoint and Word.
LexisNexis Risk Solutions (
www.lexisnexis.com/risk) is a leader in providing essential information that helps customers across all industries and government predict, assess and manage risk. Combining cutting-edge technology, unique data and advanced scoring analytics, we provide products and services that address evolving client needs in the risk sector while upholding the highest standards of security and privacy. LexisNexis Risk Solutions is part of Reed Elsevier, a leading publisher and information provider that serves customers in more than 100 countries with more than 30,000 employees worldwide.
Apply online:
https://reedelsevier.taleo.net/c ... 70&src=JB-11660
