Sr Principal Data Scientist

  • 34261
  • Life - Data Science
  • |
  • Minnesota, United States
  • |
  • Sep 20, 2018
Other
RESPONSIBILITIES
  • Manage the implementation of research investigations and construction of data products employing statistical and/or machine learning approaches.
  • Translate research questions or product requirements into actionable work.
  • Collaborate within cross-disciplinary research teams including business-facing leaders and expert domain scientists.
  • Provide recommendations and guidance to project leads based on insights gleaned from data.
  • Communicate effectively and clearly to a range of audiences, from domain scientists to business professionals.
  • Work effectively as both an individual analytic contributor while also mentoring and leading less experienced data scientists.
  • Analytical, articulate, inquisitive, and insightful.
  • A passion for problem solving using data and analytics.
QUALIFICATIONS
  • Master’s / PHD degree in Math, Applied Math, Statistics, Computer Science, Physics, or related field.
  • 7+ years of experience in data science position/s.
  • Experience with inferential statistics and predictive modeling.
  • Experience applying a broad range of statistical, mathematical, or computational approaches using data to solve problems.
  • Proficiency in model-building language such as R or Python.
  • Experience leading data science efforts and working with non-technical leaders.
  • Experience mentoring and leading junior data scientists.
  • Experience translating research questions or business requirements into actionable work.
  • Working knowledge of relational databases and database structures.
  • Experience applying machine learning algorithms to build predictive models OR statistical modeling.
  • Preferred Qualifications:
  • Experience working in the healthcare industry.
  • Experience extracting data from structured and unstructured environments.
  • Experience in NLP, neural networks, or Bayesian Analytics.
  • Experience informing experimental design using historical data.