Date: Jun 27, 2019

Location: New York, NY, US

Company: New York Life Insurance Co


A career at New York Life offers many opportunities. To be part of a growing and successful business. To reach your full potential, whatever your specialty. Above all, to make a difference in the world by helping people achieve financial security. It’s a career journey you can be proud of, and you’ll find plenty of support along the way. Our development programs range from skill-building to management training, and we value our diverse and inclusive workplace where all voices can be heard. Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and service, supported by our Foundation. It all adds up to a rewarding career at a company where doing right by our customers is part of who we are, as a mutual company without outside shareholders. We invite you to bring your talents to New York Life, so we can continue to help families and businesses “Be Good At Life.” To learn more, please visit LinkedIn, our Newsroom and the Careers page of


Position:  Senior Associate-Senior Database Scientist  (New York, New York)

Duties: Lead and contribute to data analysis and modeling projects from project/sample design, business review meetings with internal and external clients deriving requirements/deliverables, reception and processing of data, performing analyses and modeling to final reports/presentations, communication of results and implementation support Provide technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals. Utilize advanced statistical techniques to create high-performing predictive models and creative analyses to address business objectives and client needs Develop and Test new statistical analysis methods, software and data sources for continual improvement of quantitative solutions Research conference materials, books, journals and publications to identify and implement the most appropriate framework/ methodology to use for our business use cases. Utilize data wrangling/data matching/ETL techniques while programming in several languages to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets Deploy analytical solutions in production systems. Communicate with internal stakeholders on product design, data specification, model implementations, with partners on collaboration ideas and specifics, with clients and account teams on project/test results, opportunities, questions. Create project milestone plans to ensure projects are completed on time and within budget Ensure compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects

Requirements: Master’s degree in Statistics, Computer Science or Mathematics or related field (willing to accept foreign education equivalent) plus three years of  relevant industry full-time experience performing data analytics and modeling in insurance pricing, underwriting, and fraud detection or related areas. Or, alternatively, a  PhD degree in Statistics, Computer Science or Mathematics or  related field (willing to accept foreign education equivalent) and one year of  relevant industry full-time experience performing data analytics and modeling in insurance pricing, underwriting, and fraud detection or related areas.  Must possess one/three years of the following: programming in SAS (STAT, macros, EM), R, Python, SPARK, and SQL. Using GitHub/GitLab code sharing/collaboration tools; performing data wrangling, data matching, and ETL techniques while programming in several languages (R, Python, SAS, SQL and Spark) to extract and transform data from a variety of data sources (Oracle, SQL, Hadoop); performing statistical modeling techniques including linear regression, logistic regression, survival analysis (Cox proportional hazard models), Generalized Linear Models (GLM), Robust GLM, regularization techniques (Ridge, Lasso, ElasticNet), decision tree-based models (Random Forests and GBM), cluster analysis, and Principal Component Analysis (PCA); performing variable selection, feature creation (transformation, binning, high level categorical reduction, etc.) and model validation and testing (hold-outs, CV, bootstrap); performing outlier detection, robust statistical modeling (e.g. rank based regression), design and analysis of experiments, hypotheses testing, convex and non-convex optimization and partial least squares regression; deploying analytical solutions in production systems and participating in proof of concept tests of new data, software and technologies; performing data visualization using R Shiny, Spotfire and Tableau, and interfacing with business partners including Product Actuaries, Corporate Finance, Product, Pricing, Data Strategy, and Sales to analyze data needs.



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