Data Scientist

  • 38173
  • Non-Life - Data Science
  • |
  • Ontario, Canada
  • |
  • Sep 14, 2020
Insurance
RESPONSIBILITIES
  • Use large volume structured and unstructured data from various data source systems leveraging various data exploration, data discovery, visualization and reporting tools and techniques.
  • Responsible for collecting, parsing, visualizing and analyzing data sets from multiple sources to build prescriptive analysis and recommendations using predictive models or ad-hoc data explorations.
  • Code, test and deploy analytics pipelines to create robust and scalable analytical applications to drive customer-centric automation and constantly improve finance and risk reporting.
  • Ensures that analytics pipelines are auditable, secure and will serve multiple user groups within the Company.
  • Responsible for translating complex functional and technical requirements into detailed design documents to build analytical applications and dashboards to drive automation and self-serve capabilities.
  • Work in collaboration with various business functions in an agile development environment with multiple priorities and demanding timelines.
  • Contribute to the knowledge body with centralized documentation and code bank and facilitate workshops for the end users.
QUALIFICATIONS
  • Master’s degree in Finance/Economics/Mathematics/Statistics/Actuary/Computer Science/IT or any other data and compute intensive field.
  • Code efficiently in R, Python, JScript, Shell Scripting, SQL and proficient in Linux OS. Awareness of C/C++/Java/SAS would be of value.
  • Hands-on experience with one or more Machine Learning and Deep Learning open-source libraries, e.g. Scikit-learn, Spark MLLib, H2O, TensorFlow, Keras etc.
  • Hands-on experience with various analytical solution deployment frontend and backend tools such as Flask, Redux/Postgres/SQLAlchemy, Nginx, React.js, Redux, CSS, Auth0, Docker and Kubernetes.
  • Hands-on experience with data architecture, data modeling, data pipelining, serverless computing and parallel processing.
  • Working knowledge of design thinking and agile prototyping to develop and test business use cases and analytical applications.
  • Ability to communicate results/analysis/visualization precisely and clearly to non-technical users from various business functions.
  • Passionate about learning new tools and techniques, and open to share knowledge/learnings with peers.
  • Work with minimal direction and problem-solving mindset with the proven ability to co-ordinate tasks.