Data Scientist - Actuarial and Product Analytics
New York Life Insurance Company ("New York Life" or "the company") is the largest mutual life insurance company in the United States. Founded in 1845, New York Life is headquartered in New York City, maintains offices in all fifty states, and owns Seguros Monterrey New York Life in Mexico.
New York Life is one of the most financially strong and highly capitalized insurers in the business. The company reported 2016 operating earnings of $1.954 billion. Total assets under management at year end 2016, with affiliates, totaled $538 billion. As of year-end 2016, New York Life's surplus was $23.336 billion. New York Life holds the highest possible financial strength ratings currently awarded to any life insurer from all four of the major ratings agencies: A.M. Best, A++; Fitch AAA; Moody's Aaa; Standard & Poor's AA+. (Source: Individual Third Party Ratings Report as of 8/17/16).
Financial strength, integrity and humanity-the values upon which New York Life was founded-have guided the company's decisions and actions for over 170 years.
The Center for Data Science and Analytics 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. Analytics (multivariate models) support of Actuarial assumptions (Mortality and Lapse), 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?
You will apply your highly developed analytical skills to work 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 technical data/ETL/programming skills to ingest, wrangle and explore external and internal data to create reports, function as the data expert and prepare data for modeling and support production deployment of models OR apply your technical modeling skills to build world-class predictive models for solving tangible business problems.
You will apply your high energy level and business sense to communicate with internal stakeholders and external vendors while effectively contributing to complex analytics projects.
Utilizes 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. Deploys analytical solution in production systems.
- Master's degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, quantitative psychology, or operations research and 3 years of relevant industry experience
OR Ph.D. with concentration in similar fields
OR Associateship/Fellowship in one of the Actuarial Societies and 5 years of relevant industry experience
- Strong verbal and written communications skills, listening and teamwork skills, and effective presentation skills. This is absolutely 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 almost all of the following: SAS (STAT, macros, EM), R, H2O, Python, SPARK, SQL, other Hadoop. Exposure to GitHub.
- Strong expertise in statistical modeling techniques such as linear regression, logistic regression, survival analysis, GLM, tree models (Random Forests and GBM), cluster analysis, principal components, feature creation, and validation. 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).
Expertise in database systems (Oracle, Hadoop, etc.), ETL/data lineage software (Informatica, Talend, AbInitio), data modeling and data governance. Experience with insurance or consumer financial data is a plus.
- Experience with data visualization (e.g. R Shiny, Spotfire, Tableau)
- Proficiency in creating effective and visually appealing PowerPoint presentations.
Location: Manhattan (midtown, walking distance from Penn Station and Grand Central)
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Based on revenue as reported by "Fortune 500, ranked within Industries, Insurance: Life, Health (Mutual)," Fortune Magazine, June 17, 2016. See http://fortune.com/fortune500/ for methodology.
Total surplus, which includes the Asset Valuation Reserve, is one of the key indicators of the company's long-term financial strength and stability and is presented on a consolidated basis of the company.
1. Operating earnings is the key measure use by management to track Company's profitability from ongoing operations and underlying profitability of the business. This indicator is based on generally accepted accounting principles in the US (GAAP), with certain adjustments Company believes to be appropriate as a measurement approach (non GAAP), primarily the removal of gains or losses on investments and related adjustments.
2. Assets under management represent Consolidated Domestic and International insurance Company Statutory assets (cash and invested assets and separate account assets) and third party assets principally managed by New York Life Investment management Holdings LLC, a wholly owned subsidiary of New York Life Insurance Company.
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