Job Overview

Toronto, Ontario
Job Type
Full Time
Salary / Compensation
$89,000 - $133,600 Per Year
Date Posted
9 months ago

Additional Details

Good Exp. Required (5 - 9 Years)

Job Description

The Data Architecture & Analytics Services team plays a key role in enabling EDC’s digital business ambitions. In doing so, we require advanced skills that will help us execute the data strategy, leverage new methods and tools by designing solutions that will move us to a data driven, digital organization. We are looking for a highly capable Machine Learning Engineer, who will drive the implementation of our Machine Learning Operations (MLOps) framework and take ownership of its ongoing operations and evolution taking into considerations technical, governance and organizational implications. The ideal candidate will have the technical knowledge, strong collaboration, leadership and influencing skills and a solid understanding of how to design innovative solutions. Reporting to the Director, Data Architecture & Analytics Services, the Machine Learning Engineer will be responsible for taking data science models, helping scale them out to production and ensuring that business SLAs are adhered to while enabling continuous feedback loop that will be vital for enhancing the validity and accuracy of analytics workloads

This is a unique opportunity for a results-focused individual with strong interpersonal and strategic thinking skills to help mature Analytics Operations/Machine Learning Operations at EDC. If you also have knowledge of data science and software engineering, we’d like to meet you.

Key Responsibilities

  • Consulting with stakeholders to determine and refine analytics/machine learning objectives
  • Configuring API, Batch and other model serving endpoints to integrate or embed model outputs into relevant business operations
  • Setting and operating version control solution for data, code and models ensuring proper tracking between versions
  • Turning unstructured data into useful information by auto-tagging images and text-to-speech conversions
  • Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks
  • Running tests, performing statistical analysis, and interpreting test results
  • Documenting machine learning processes and mentor/coach impacted analytics delivery teams
  • Optimizing machine learning pipelines
  • Verifying and standardizing machine learning components such as engineered features, text normalizers, etc., to ensure reusability
  • Automating data gathering, model building and model serving processes
  • Collaborating with data scientists to streamline the process of delivering ML models into production by using software engineering best practices for testing, tuning and optimization
  • Optimizing ML models for performance, scalability and reliability with a focus on continuous improvement
  • Interfacing with data engineers to ensure there is an automated data pipeline and adequate data integrity for building and ensuring continuous operation of analytics models
  • Working with security, privacy and compliance specialists to have the full ML pipeline secured and documented in compliance with relevant regulations
  • Keeping abreast of developments in machine learning and new tools that can be used for improving MLOps processes
  • Automating the deployment of analytics models from sandbox to production environments
  • Designing self-running monitoring systems to automate tracking of analytics model performance
  • Implementing and managing appropriate machine learning algorithms and libraries in our analytics platforms
  • Advising on the design and development of analytics/machine learning solutions to ensure consistency of implementations
  • Champions and presents the technical vision to the executive team and business stakeholders
  • Manages and delivers work using agile methodologies (i.e. Kanban, Scrum, etc.)

Screening Criteria

  • Undergraduate degree in computer science, data science, mathematics, or a related discipline
  • At least two years' experience as a machine learning engineer or five years’ experience in a related role such as Data Scientist, Data Engineer or Software Engineer
  • Minimum five years’ experience code development with SQL, Python, Java, and/or R.
  • Extensive knowledge of machine learning frameworks (such as Keras, TensorFlow, Spark ML, etc.), data structures, data modeling, and software architecture
  • In-depth knowledge of mathematics, probability, statistics, and algorithms
  • Outstanding analytical and problem-solving skills
  • Experience working in an Agile environment
  • Knowledge of distributed computing systems
  • Proficient in working with SAS Viya including CAS action programming with CASL, R, and Python, SAS Visual Analytics, SAS Visual Statistics, and SAS Visual Data Mining and Machine Learning
  • Familiarity with Azure ML, Azure Data Lake Storage, Azure Synapse Analytics, Databricks, and Spark
  • Superb analytical and problem-solving abilities
  • Exceptional skills in coaching, influencing and driving cross-functional teams, and delivering solutions in a highly complex, dynamic and nebulous environment
  • Excellent verbal and written communication, critical and strategic thinking, time management, priority planning and interpersonal skills with a continuous learning mindset
  • Knowledge of Continuous Integration / Continuous Delivery (CI/CD) methods


  • MBA in a related business field
  • Project Management Experience
  • Bilingualism in both official languages (English & French)
  • Experience in various analytics technologies such as Azure Data Factory, Synapse, Databricks, ADLS, PowerBI, SQL Server/Oracle DBMS, Azure Purview, Azure Event Hub/Grid, Spark, Azure Databricks, SAS Viya, Azure Portal

Salary Range

  • $89,000 - $133,600, plus performance-based incentive 


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