Christopher Ewaschuk
Quality Analytics Engineer @Ford Motor Company
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WORK HISTORY
Quality Analytics Engineer @Ford Motor Company
Fleet wide vehicle monitoring of in-vehicle software stability using machine learning model (SWORD)- Supervised learning using in-vehicle software stability to predict probability of vehicle repair/warranty needed ($1.4M savings)- 95% accuracy with timeseries forecasting of Software performance metrics using Deep Neural Networks (DNN), XG-Boost - Design and implementation of customized advanced analytics applications such as data driven metrics for a Product Quality Index to evaluate and monitor the quality and performance of embedded software on electronic control units in Ford vehicles including the F-150 and Bronco (coded in Python)- Formulation, calculation, and reporting of DevOps metrics (DORA) for software development teams & management applied to embedded systems (coded in Python)- Identifying causal relationships in data via Machine learning with Matlab, Python, DataRobot, Tensor-Flow/Keras & process analytics via Minitab - Review and quality control of production code enabling robust deployment - End to end Machine Learning application development, deployment, monitoring, and maintenance: Python, Github Actions, Datarobot, Postgres SQL, Qlikense - Creating robust data pipelines for ETL data with SQL, MySQL, Rest API, Google BigQuery, Jenkins, SonarQube - Working under the Agile model via JIRA and Atlassian Confluence
EDUCATION
Faculty of Engineering - McMaster University
Bachelor of Engineering and Management (B.Eng.Mgt), Chemical Engineering
Faculty of Engineering - McMaster University
Masters of Applied Science (M.A.Sc), Process Systems Engineering
ABOUT CHRISTOPHER EWASCHUK
Experienced Machine Learning Engineer & Advanced Process Control Engineer with a proven…
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