Data Scientist

  • 35068
  • Non-Life - Data Science
  • |
  • Massachusetts, United States
  • |
  • Apr 17, 2019
Insurance
RESPONSIBILITIES
  • You will work with internal and external data to understand and accurately predict: Expected insurance losses; Shopping behavior; Retention behavior; Most compelling value propositions; Awareness and consideration of brand; Quote and conversion rates; Retention and lifetime customer value; Contribution to overall margin; Appropriate and actuarially sound risk pooling; Loss cost and claim frequency trends / inflation; Customer satisfaction; Routing and service step-by-step design; Fraud detection and handling strategy; Unpriced risk existence and tracking; and Other drivers of success you identify worthy of analysis.
  • You will need to find and understand various internal and external data sources as well as obtain and cleanse such data to make it usable and to extract, clean, and manipulate large datasets for model building.
  • You will develop illicit hypothesis from internal personnel regarding key variables and test those hypotheses using available data.
  • You will design, build and append to a data warehouse and DataMart connecting data with common categorical and identifying descriptors, and make the data and your derived analytical measures to production systems, underwriters and other personnel.
  • You will develop and enhance models used in a variety of contexts within the product management structure of the company by utilizing both statistical methods and machine learning techniques.
  • You will stay current on the latest machine learning and big data trends and be responsible for advancing the modeling capabilities within the company and transferring those advancements to other personnel.
  • You will facilitate the application of models developed considering the regulatory, distribution and competitive contexts within which we operate.
  • You will work with business sponsors and IT teams to implement analytic solutions.
QUALIFICATIONS
  • An advanced degree in mathematics, machine learning, economics, statistics or a related field.
  • Knowledge of new and innovative modeling and statistical techniques - a relevant specialty is preferred.
  • Attention and desire to work on large data crunching projects.
  • Strong written and oral communication skills and a proven ability to communicate technical concepts to both technical and non-technical audiences.
  • High standards for the work products delivered.
  • Creative thinker with the ability to synthesize information from various sources and apply that information to concrete business problems.
  • Ability to influence and guide across departmental boundaries.
  • Flexibility and resiliency characteristic of a professional.
  • Ability to work with and understanding of the merits of various data structures.
  • Proficiency with the latest modeling software and techniques, such as SAS, R, Python or Tableau.
  • Passionate about tackling sample bias, over-fitting, variable selection, missing values, etc.
  • Ability to understand the need to balance predictive power, interpretability, and ease of implementation.