Senior Manager / Data Scientist

  • 35499
  • Non-Life - Data Science
  • |
  • Ontario, Canada
  • |
  • Jul 16, 2019
  • Responsible for proactively identifying AI and Machine Learning opportunities within Client portfolios, building, and validating, deploying and monitoring advanced descriptive, predictive and prescriptive machine learning models, including Risk Management, Customer Experience and Journey analytics, Digital Analytics, Customer Segmentation, Customer Acquisition, Churn Management, Next Best Action and many others.
  • Effectively partner with Clients to understand and anticipate their needs and guide them towards building AI and Machine Learning solutions delivering both short-term wins and identifying long-term opportunities for managed data science services.
  • Build, validate, deploy, monitor and provide DevOps support for a set of AI/Machine Learning models and services for both internal POCs and Client production initiatives, including Risk Management, Customer Experience and Journey analytics, Digital Analytics, Customer Segmentation, Customer Acquisition, Churn Management, Next Best Action and many others.
  • Leverage commercial and open source AI, Machine Learning, Big Data ecosystem tools, BI, Visualization and Discovery tool to deliver advanced models and other Data Science solutions. Specific tools will include, full SAS stack, R, Python, Scala, Java, Spark SQL/ML/MLLib/GraphX, Data Science Notebooks and Workbenches, Azure Machine Learning, Tableau, Datameer, and other tools.
  • Proactively monitor and tune AI / Machine Learning model performance, manage champion/challenger models to ensure optimal performance and resource utilization.
  • Ensure all AI/Machine Learning models are properly packaged and documented for deployment. Participate in client training and knowledge transfer as required.
  • Ensure all solutions comply with the highest levels of security, privacy and data governance requirements as outlined by the company and Client legal and information security guidelines, law enforcement, and privacy legislation, including data anonymization, encryption, and security in transit and at rest and others as applicable.
  • Effectively leverage continuous integration, continuous development and continuous deployment agile and DevOps tools and processes to deliver and support advanced Data Science and Big Data solutions and services, including Git, Jira, Jenkins and others as required.
  • Acts as a Subject Matter Expert and a Thought Leader, continuously following industry trends, the latest competitive developments, and delivering papers and presentations at major industry conferences and events.
  • A degree in AI, Machine Learning, Statistics, Economics/Econometrics, Computer Science, Engineering or equivalent.
  • Data Scientist certification, including Hadoop, Spark or equivalent production experience.
  • Proficiency and an in-depth understanding of the Predictive Modeling Lifecycle and best practices for feature engineering, model development and tuning, model validation, model deployment packaging, model management and performance monitoring.
  • 7+ years of extensive experience building Predictive Machine Learning and AI models for Customer Experience, Customer Journey analytics, Customer Segmentation, Churn modeling, Lookalike Modeling or equivalent use cases.
  • 5+ years of experience with Text Mining, NLP and Sentiment Analysist.
  • 5+ years of production experience with SAS platform, including, Base, Stat, ETS and other core packages, client tools - Enterprise Guide, Enterprise Miner, Visual Analytics, Visual Statistics, SAS Studio.
  • Experience running SAS 9.4 on Hadoop - SAS/Access or in-database SAS HPA.
  • Experience with Open source AI / Machine Learning / Data Science tools - R, Python, Spark. Including experience working with Notebooks and Data Science Workbenches.
  • Experience with Spark-based machine learning, including Spark SQL, ML, MlLib, GraphX.
  • Experience with Deep Learning on CPUs and GPUs, including ANN, CNN, RNN frameworks like TensorFlow/Keras, MXNet or equivalent would be an asset.
  • Experience with Azure Machine Learning would be an asset.
  • Experience with SAS Risk and Finance modeling modules and workbenches would be an asset.
  • Experience with SAS Decision Manager would be an asset.
  • Experience with SAS Text Mining and Sentiment Analysis would be an asset.