Data Scientist

  • 38661
  • Non-Life - Data Science
  • |
  • Ontario, Canada
  • |
  • Nov 30, 2020
Insurance
RESPONSIBILITIES
  • The incumbent will closely collaborate with model owners and stakeholders across Finance, Risk, Operations and IT teams on model validation.
  • Thorough technical validation of code and documentation of ETL and data pipelines, predictive models and its deployment.
  • Provide validation findings and recommendation to the modelers.
  • Develop a champion-challenger ML/Bayesian/DL/AI model as needed by the business.
  • Support the Director, Model Risk Governance in documenting technical findings for presentation to validation groups and Company’s executive team.
  • Oversee and ensure an end-to-end ML/Bayesian/DL/AI model lifecycle is built in accordance to company’s model risk management framework and industry best practices.
  • Assist pipeline automation and other risk analytics efforts.
QUALIFICATIONS
  • Advanced degree in highly quantitative field (Applied Statistics, Applied Mathematics, Applied Physics, Computer Science, Operational Research, Applied Finance, Econometrics, Actuarial, Engineering, etc). PhD is preferred.
  • Minimum 2 years of work experience in ML/Bayesian/DL/AI model development and deployment. Experience in model risk, quantification and validation is required.
  • Strong programming fundamentals and advanced knowledge in Python, Pandas, R, PySpark, SQL and similar tools.
  • Solid understanding of various ML/Bayesian/DL/AI algorithms and libraries, e.g. Scikit-learn, Spark MlLib, TensorFlow, Keras, PyTorch, RL etc..
  • Experience with data visualization tool, particularly Tableau.
  • Experience with AWS and Microsoft Azure cloud services.
  • Experience with containerization and orchestration tools, particularly Dockers and Kubernetes.
  • Experience working with advanced Git Workflows (pull requests, code reviews, issues, and branching), Jira, and Confluence.
  • Familiarity with any microservices such as Flask, Plumber, React, NodeJS, etc..
  • Familiarity with Unix environment and scripting.
  • Strong written and verbal communication skills enabling the translation of technical results into non-technical explanations.
  • Fast-learner, proactive, results-oriented, strong problem-solving attitude, and a team player.