Data Scientist

  • 36474
  • Non-Life - Data Science
  • |
  • Quebec, Canada
  • |
  • Dec 16, 2019
Insurance
RESPONSIBILITIES
  • Interact with a large number of stakeholders working in a variety of fields to analyze key needs. Develop and automate analytical models to meet those needs.
  • Formulate research questions and analyze Big Data from all sources and a range of formats using advanced statistical methods.
  • Conduct profiling, inspect data quality and select predictors so they can be used in predictive modelling.
  • Explain and communicate analysis results in a concise manner.
  • Document and retain inferential models to support the organization’s operational and strategic decisions.
  • Develop, train and optimize machine learning algorithms on Big Data.
  • Develop pipelines for data preparation and the selection of predictors, and ensure the deployment, assessment, logging and management of predictive algorithms.
  • Conduct research and experiments using available machine learning tools and recommend new tools to proactively meet the needs of key sectors in the organization.
  • Design prototypes containing algorithms with innovative data and optimize existing methods.
  • Work closely and effectively with data engineering specialists in managing algorithms, tools and the Big Data ecosystem.
QUALIFICATIONS
  • Bachelor’s degree in IT, statistics, econometrics or a related field (e.g., mathematics, physics, engineering)
  • Master’s degree in IT, statistics, econometrics or a related field (e.g., mathematics, physics, engineering), an asset
  • 8 years of relevant work experience in the field of data science, ideally in the financial services industry
  • 3 or 4 years of experience as a data scientist (an asset)
  • Extensive knowledge of statistical methods and machine learning algorithms (e.g., regression, classification, clustering, dimensionality reduction and deep learning)
  • Proficiency in a programming language (e.g., Python, Scala, Java), a statistical programming language or a data mining language (e.g., R, SAS, SPSS), as well as SQL
  • A practical understanding of Python machine learning libraries (e.g., Pandas, NumPy, Scikit-learn) or R and deep learning (e.g., TensorFlow, Torch)
  • Strong skills in robust pipeline development and reproducible research
  • Strong skills in Big Data development tools: Hadoop, Spark, PySpark or SparkR
  • Strong skills with visualization tools (e.g., Power BI, Tableau, Shiny)
  • Sound knowledge of data science development tools (Jupyter, RStudio), ideally on a platform such as IBM Watson Studio, Anaconda, Databricks or GCP)
  • Knowledge of management and productivity environments and tools (e.g., Jira, Git, Bitbucket, Jenkins)
  • Comfortable working on and collaborating with an Agile team (e.g., Scrum)
  • Strong communication skills for a technical and non-technical audience
  • Sound practical knowledge of the following libraries and languages: StanfordNLP, TensorFlow, PyTorch, Keras, MXNet, H2O (an asset)
  • Sound knowledge of visualization tools (e.g., Power BI, Tableau), an asset