Data Scientist - Actuarial and Product Analytics

New York City, NY, United States Full Time Mid-Level

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.

Responsibilities
  1. 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.
  2. 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.
  3. Utilizes advanced statistical techniques to create high-performing predictive models 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.
AND/OR

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.

  1. 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.
  2. 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.
  3. 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.
  4. Assures compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects.
  5. Travels to events and vendor meetings as needed (< 10%).
Required qualifications
  • 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).

OR

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.
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