Vacancy expired!
When you join New York Life, you're joining a company that values career development, collaboration, innovation, and inclusiveness. We want employees to feel proud about being part of a company that is committed to doing the right thing. You'll have the opportunity to grow your career while developing personally and professionally through various resources and programs. New York Life is a relationship-based company and appreciates how both virtual and in-person interactions support our culture.
Senior Data Scientist, Underwriting (Remote) New York Life, the largest writer of retail life insurance in the U.S. and a top player in annuities, long-term care and mutual funds, is seeking a Senior Data Scientist in its Center for Data Science and Artificial Intelligence (CDSAi). The company has 177 years of history and while usable data does not quite go back that far, we have a wealth of internal information on consumers, policies and their performance, as well as applicants, prospects and our 12,000 agents. We also have a multitude of external data from a great variety of sources. Analytical challenges range from mortality risk to agent recruiting decisions, service optimization, consumer analytics (segmentation, response, conversion, retention, up-sell), fraud detection, advertising allocation and office footprint optimization. The Center for Data Science and Artificial Intelligence (CDSAi) is the 60-person innovative corporate Analytics group within New York Life, led by Chief Analytics Officer Glenn Hofmann ( https://www.linkedin.com/in/glennhofmann/ ). We are a rapidly growing entrepreneurial department which designs, creates, and offers innovative data-driven solutions for many parts of the enterprise. For more information on CDSAi, please visit our website ( https://www.newyorklife.com/careers/corporate/data-science ). In the 6 years of the existence of CDSAi we have built a lot of predictive modeling solutions that are being used by various areas in the company. We have also stood up a modern model deployment platform that allows our models to be accessed in real time or batch (via APIs) from any production system in the company. The role reports to Nalin Aggarwal ( https://www.linkedin.com/in/nalin-aggarwal-frm-816a3716/ ), who currently leads the Underwriting Data Science team. A good understanding of predictive analytics (including the process of building and deploying models) and technology is essential. Examples of Underwriting Data Science products are below. There are several additional ones, and more products will follow, especially morbidity and group insurance products.- Underwriting mortality risk scores
- Accelerated Underwriting models
- Natural language processing on medical text documents
- Leads and contributes 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.
- Demonstrates to internal and external stakeholders how analytics can be implemented to maximize business benefits. Provides technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals.
- Utilizes advanced statistical techniques to create high-performing predictive models using R or Python, and creative analyses to address business objectives and client needs.
- Tests new statistical analysis methods, software and data sources for continual improvement of quantitative solutions.
- Proactively and effectively communicates in various verbal and written formats 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.
- Resolves problems and removes obstacles to timely and high-quality project completion. Create project milestone plans to ensure projects are completed on time and within budget. Provides high quality ongoing customer support; answering questions, resolving problems and building solutions.
- Validate ongoing data science projects, including coding scripts and summary results for data manipulation/cleaning and modeling. Challenge existing method adopted in the project with alternatives. Provide feedback to improve the deliverables.
- Teach and explain basic data science concepts and tools to general internal audience.
- Follows industry trends in insurance and related data/analytics processes and businesses.
- Functions as the analytics expert in meetings with other internal areas and external vendors. Actively participates in proof-of-concept tests of new data, software and technologies. Shares knowledge within Analytics group.
- Assures compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects.
- Travels to events and vendor meetings as needed (
- Master's degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, or machine learning and 3 years of relevant industry experience OR Ph.D. with concentration in similar fields.
- Hands-on experience in coding and predictive modeling using large and complex datasets in a business setting.
- Strong verbal and written communications skills, listening and teamwork skills, and effective presentation skills. This is essential since you will have a lot of exposure to different internal groups (data, IT, actuarial, medical, underwriting, Legal, Agency, government relations, etc.) as well as third-party data partners.
- Substantial programming experience with Python, R, SQL, and/or SPARK. Experience with data wrangling, data matching, and working with GitHub.
- Strong expertise in supervised and unsupervised machine learning models ( e.g. , XGBoost, deep learning, k-means). Strong expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.) and validation (hold-outs, CV, bootstrap). Experience with data visualization (e.g. R Shiny, Spotfire, Tableau).
- Experience using open source tools and libraries for Natural Language Processing, entity recognition, topic identification, and Regular Expression is a plus.
- Deep statistical knowledge and experience in survival analysis is a plus.
- Experience with insurance or consumer financial data is a plus.
- Manhattan (NoMaD, walking distance from Penn Station and Grand Central Station).
- Relocation is available.
- Onsite 3 days per month; otherwise, fully remote.
Vacancy expired!