Data Scientist - Finance, Actuarial and Product Analytics

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Date: Jan 11, 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.

 

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. We are designing and building multivariate models to facilitate the setting of of Actuarial assumptions (Mortality and Lapse). These are just a couple out of several projects with large business value. Product & Sales Analytics to understand the impact of product pricing and restructuring on sales, Financial Analytics to understand the uncertainty around key financial indicators, 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. actuarial, finance, underwriting, pricing, product development, agent recruiting, prospecting).

 

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. Lead and contribute 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. Provide technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals.
  3. Utilize advanced statistical techniques to create high-performing predictive models and creative analyses to address business objectives and client needs.
  4. Develop and Test new statistical analysis methods, software and data sources for continual improvement of quantitative solutions.
  5. Utilize 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.
  6. Deploy analytical solutions in production systems.
  7. Communicate 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.
  8. Create project milestone plans to ensure projects are completed on time and within budget.
  9. Ensure compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects.
  10. Assures compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects.
  11. 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

 

a 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/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.
  • 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, ElasticNet), 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 Product Actuaries, Corporate Finance, Product, Pricing, Data Strategy, and Sales to analyze data needs.

     

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

     

    EOE M/F/D/V

     

    If you have difficulty using or interacting with any portions of this Web site due to incompatibility with an Assistive Technology, if you need the information in an alternative format, or if you have suggestions on how we can make this site more accessible, please contact us at: (212) 576-5811.

     

    *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.

     

EOE M/F/D/V

 

If you have difficulty using or interacting with any portions of this Web site due to incompatibility with an Assistive Technology, if you need the information in an alternative format, or if you have suggestions on how we can make this site more accessible, please contact us at: (212) 576-5811.


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