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 www.NewYorkLife.com .
The Center for Data Science and Artificial Intelligence is the innovative corporate Analytics group within New York Life. We are a rapidly growing entrepreneurial department which aims to design, create and offer innovative data-driven solutions for many parts of the enterprise. We are aided by New York Life-s existing business with a large market share in individual life insurance. We have the freedom to explore external data sources and new statistical techniques, and are excited about delivering a whole new generation of Analytical solutions.
In fact, we are designing and will build one of the first multivariate model-based continuous risk differentiations in the industry. This model will incorporate current underwriting best practices (including medical rules) as features and add other data sources, patterns/ideas and variables to essentially create a rating plan to support the next generation underwriting process at New York Life. This is just one of several projects with large business value. Geographic analytics on agents and customers, application fraud detection, agent success prediction and client prospecting analytics (off-line and on-line) are other exciting examples of enormous incremental value from analytics. Our products will be implemented into real-time core business processes and decisions that drive the company (e.g. underwriting, pricing, agent recruiting, prospecting, new product development).
We work with data ranging from demographics, credit and geo data to detailed medical data (medical test results, diagnosis, prescriptions) and social media information. We have a modern computing environment with a solid suite of data science/modeling tools and packages, and a large (but manageable) group of well-trained professionals at various levels to support you. Life insurance is on the verge of huge change. This is a chance to be part of, actually to drive, the transformation of an industry. Is this not why we became data scientists?
These models must be validated to ensure they effectively address the business needs and that assumptions are thoroughly understood. This position sits on the first line. The Model Validator should be able to understand a wide range of models to effectively challenge the model development process by assessing: the suitability of the chosen methodology, that the proper testing has been performed, alternative modeling approaches to the one proposed, the most adequate metrics for model accuracy and robustness, that the model has been properly implemented and that the model properly monitored. All these activities are performed and delivered in an environment that promotes the creation of value and constant improvement cycle.
You will apply your highly developed analytical skills to validate models that touch on all aspects of the life insurance value chain, ranging from risk models, fraud detection, process triaging, and marketing predictions to a variety of other analytics solutions. You will apply your high energy level, communication skills and business sense to communicate with model developers and internal stakeholders.