Data Scientist I

  • 35950
  • Non-Life - Data Science
  • |
  • Ontario, Canada
  • |
  • Oct 1, 2019
Insurance
RESPONSIBILITIES
  • Validate Machine Learning models and AI applications.
  • Develop/implement Machine Learning model validation methodologies and standards. Ensure that the validation methodologies and standards are in line with industry best practice or address regulatory and audit requirements and/or findings in a timely manner.
  • Develop and apply a variety of statistical tests and modeling techniques to identify/recommend improvements to models and undertake related initiatives. Ensure extensive testing of model sensitivity that help assessing model behavior and risk.
  • Implement and evaluate external models used for benchmarking internal model performance. Participate in model selection and related due diligence activity.
  • Actively participate with business partners in internal data management to ensure data integrity and the completeness of data capture for model validation and development purpose.
  • Maintain full professional knowledge of techniques and developments in the field of Machine Learning and share knowledge with business partners and senior management.
QUALIFICATIONS
  • Strong quantitative skills with an advanced degree in one or more of the following areas: computer science, mathematics, physics, statistics, machine learning, economics, engineering, and/or actuarial science.
  • Up to 3 years' experience of working in analytical environments.
  • Experience with and strong knowledge of Machine Learning theory and predictive algorithms: Neural Networks/Deep Learning, NLP, Bagging and Gradient Boosting methods, Generalized Additive Models, Graphical Models, Bayesian/probabilistic methods and etc.
  • Experience or knowledge of Machine Learning Model Interpretation/Explanation, as well as Bias/Fairness assessment, tools and algorithms.
  • Experience with Big Data analytics tools and environments, such as, Hadoop/Hive, Spark, and H2O.
  • Ability to research and implement Machine Learning algorithms from academic research papers is a plus.
  • Object Oriented and Functional programming skills.
  • Proficient in one or more programming languages such as Java, Scala, Python and/or R.
  • Knowledge of neural network tools such as Tensorflow/Keras, MXNet and/or PyTorch.
  • Excellent verbal and written communication skills.
  • Quick learner who constantly works on improving their skills and expertise.
  • Good time management and multitasking skills with minimal supervision.