Data Science

  • 34783
  • Life - Data Science
  • |
  • New York, United States
  • |
  • Mar 1, 2019
Insurance
RESPONSIBILITIES
  • Independently leads 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, 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.
  • 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 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.
  • Actively contributes to analytics strategy by contributing ideas, preparing presentation material for internal stakeholders, and product design/business case materials for leadership.
  • 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.
QUALIFICATIONS
  • Master’s degree in Statistics, Computer Science or Mathematics and seven 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 five years 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, 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 to extract and transform data from a variety of data sources.
  • Performing statistical modeling techniques including linear regression, logistic regression, survival analysis, Generalized Linear Models, Robust GLM, regularization techniques, decision tree-based models, cluster analysis, and Principal Component Analysis.
  • Performing variable selection, feature creation and model validation and testing.
  • Performing outlier detection, robust statistical modeling, 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.