Data Science - Underwriting Optimization

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Date: Mar 30, 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 www.NewYorkLife.com.

 

The Center for Data Science and Analytics (CDSA) 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, Underwriting Data Science Team is 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 within the realm of CDSA. 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, 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 underwriting value chain, ranging from risk models, fraud detection, and process triaging to a variety of other analytics solutions.

You will apply your leadership experience, high energy level and business sense to supervise staff, communicate with internal stakeholders and external vendors while effectively leading complex analytics projects. You will also ingest and wrangle data, propose analytics strategy, create related business cases, drive several large initiatives, build and implement solutions at scale and give presentations as a subject matter expert.

 

Responsibilities

  • 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.
  • Responsible for insurance underwriting analytics, including exploration/consolidation of a variety of internal and external data (e.g. digital medical records, credit, social), triaging models and a variety of mortality models for automating most underwriting/risk classification decisions. Works closely with Underwriting, Actuarial, IT, Legal, Government relations and several other groups in designing, building and implementing these solutions
  • Utilizes advanced statistical techniques to create high-performing predictive models and creative analyses to address business objectives and client needs.
  • Develops and tests new statistical analysis methods, software and data sources for continual improvement of quantitative solutions.
  • 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 solutions in production systems.
  • Communicates 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.
  • Creates 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.
  • 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 (< 10%).

     

  • Required qualifications

  • Master’s degree in Statistics, Computer Science or Mathematics and three years of relevant industry full-time experience performing data analytics and modeling in insurance pricing, underwriting, and fraud detection or related areas.

 

      OR

 

PhD in Statistics, Computer Science or Mathematics and one year of relevant industry full-time experience performing data analytics and modeling in insurance pricing, underwriting, and fraud detection or related areas.

 

One to three year(s) of experience must include:

 

  • Programming in SAS (STAT, macros, EM), R, Python, SPARK, and SQL.
  • Using GitHub/GitLab code sharing/collaboration tools preferred. 
  • 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, Elastic Net), 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 or Tableau.
  • Interfacing with business partners including Underwriting, Marketing, Agency, Actuarial, Finance, Product, Pricing, Data Strategy and Sales to analyze data needs.

     

    Location

     

  • Manhattan (midtown, walking distance from Penn Station and Grand Central)

 

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