Lead Data Scientist

  • 37297
  • Non-Life - Data Science
  • |
  • Massachusetts, United States
  • |
  • Apr 28, 2020
Insurance
RESPONSIBILITIES
  • Develops best practices around extracting, analyzing, merging and constructing databases.
  • Seeks new databases, including evaluation and acquisition of external data, software, and other tools in support of key strategic initiatives.
  • Consults on management information aspects of technology initiatives.
  • Identifies the appropriate data for analysis, anticipates integrity issues and other possible roadblocks, and understands potential future uses of data beyond the task at hand.
  • Directs, performs and interprets appropriate exploratory analysis such as data mining, empirical data analysis, univariate analysis, partitioning analysis etc.
  • Provides roadmap to design models using available data, tools and programming languages, supports the implementation in a real-world framework, and establishes monitoring processes to ensure optimal predictive performance over time.
  • Shares new ideas on advanced analytical techniques and their application to the business.
  • Provides and/or independently gathers requirements from appropriate business partners for project, including necessary data for analysis to be performed.
  • Guides implementation of models in real-world framework.
  • Develops and manages preliminary project plans, ensures optimal and effective execution on deliverables within agreed upon timeframes, monitors program milestones and critical dates to identify potential jeopardy of project schedules, identifies ways to remove obstacles, communicates progress and makes recommendations to address issues, and manages any effects on related projects.
QUALIFICATIONS
  • Requires Master's Degree in Physics or Computer Science plus 5 years of statistical predictive modeling experience, including insurance-specific predictive modeling; preferably a PhD, in Statistics/Applied Statistics, Applied Mathematics, or Economics, or a related field, plus 8+ years of experience.
  • Solid understanding of database principles; experience in data manipulation and cleaning; experience in identification and resolution of data issues.
  • Advanced Excel skills and demonstrated experience in programming languages such as SAS, SQL, VBA, R, Python etc. Should be proficient in at least two of the following languages: SAS, SQL, R and/or Python.
  • Awareness of typical insurance data sources, both internal and external.