Machine Learning Engineer

Telus

Vancouver, BC, Canada Remote

Full time

Sep 19

This job is no longer accepting applications.

Ready to create innovative solutions and best practices?


Join our team


The Wireless Service Analytics & Transformation (WSAT) team’s goal is to enable best-in-class wireless experience by developing a customer centric view of service performance and enhancing business insights by leveraging advanced analytics and artificial Intelligence.


Our team consists of talent across various technology teams including network engineering, data scientists and management consultants to help achieve our goal.


Together, we will develop customer centric models to predict and prevent service degradation, discover new cost reduction/capital efficiency opportunities and model network experience.


Here’s the impact you’ll make and what we’ll accomplish together


As a skilled wireless engineering data scientist, you will combine your experience in engineering and data science to build systems that will improve the wireless network experience for our customers.


Our team takes pride in the development and deployment of AI/ML based solutions to improve customer experience. We take ownership of each step of the process: use your expertise to explore data sets, develop novel AI techniques to find new insights and finally develop automation pipelines and web-visualizations to deliver your AI/ML based solution.


As a result of your efforts our team will greatly improve wireless service for our millions of subscribers!


Here's how

  • You’re an expert in exploring datasets, using AI/ML techniques to find uncover insights (making use of supervised/unsupervised ML techniques)
  • You’re the go-to person for developing data pipelines that efficiently extract insights
  • You’re respected for your skills in developing novel visualizations using web-frameworks and data science libraries
  • You’re known for your ability to apply creativity to your designs and builds and able to deploy web applications that enable data scientists to visualize, troubleshoot and analyze service experience


Qualifications

 

You’re the missing piece of the puzzle

  • You hold a Bachelor’s degree in Electrical or Computer Engineering
  • You have 2+ years of hands-on experience applying machine learning and data science techniques using Python(including Deep learning, time series predictions, clustering algorithms, etc.)
  • You have hands-on experience with AI Ops and machine learning pipelines (AirFlow, Kafka, etc...)
  • You have a solid understanding of databases and big data, including: MySQL, NoSQL databases (e.g. MongoDB, Splunk, Cassandra & Hadoop)
  • You have hands-on experience with data visualization using custom frameworks (e.g. D3.js) and Tableau

Great-to-haves

  • Knowledge of high performance computer architecture
  • Knowledge of real time streaming analytics such as Spark
  • Knowledge of computer vision techniques (e.g. CNN)

Who is TELUS?


We're a high-performing team of individuals who collectively make TELUS one of the leading telecommunications companies in Canada. Our competitive consumer offerings include wireline, wireless, internet and Optik TV™. We also deliver a compelling range of products and services for small, medium and large businesses; and have carved out a leadership position in the health, energy, finance and public sector markets with innovative industry specific solutions.


Everyone belongs at TELUS. It doesn’t matter who you are, what you do or how you do it, at TELUS, your unique contribution and talents will be valued and respected. Because the more diverse perspectives we have the more likely we are to crack the code on what our customers want and our communities need.


Do you share our passion?


At TELUS, you create future friendly® possibilities.


At TELUS, we are committed to diversity and equitable access to employment opportunities based on ability.


Primary Location: CA-BC-Vancouver

Other Locations: CA-BC-Burnaby, CA-QC-Montreal, CA-AB-Calgary, CA-ON-Toronto, CA-AB-Edmonton


Schedule: Full-time

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