Data Scientist

TELUS•Edmonton, AB

About The Position

Be a part of a transformational journey with innovative talent and leading edge technologies. Join our team and what we'll accomplish together. This is an exciting opportunity to join the Systems Simplification Innovation Hub within Systems Simplification Team. We are a dynamic and agile team revolutionizing TELUS operations by designing data-driven solutions that optimize OpEx, CX, sales, billing, and other key metrics across our national workforce. Our Data Analytics team is the go-to destination for analytical, creative professionals passionate about developing their talents while solving some of TELUS' most significant challenges.

Requirements

Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative discipline — or a PhD in a relevant field.
3+ years of experience designing and implementing data architectures and analytics solutions in production environments, delivering measurable business impact.
Advanced proficiency in Google Cloud Platform (GCP), with hands-on experience in BigQuery, Workflows, Cloud Scheduler, Dataproc, and Batch jobs.
Expert-level experience with Looker Studio for creating dashboards, reports, and data visualizations that drive business insights and support decision-making.
Strong foundation in data architecture and ETL/ELT design, with the ability to optimize data pipelines for performance, scalability, and cost efficiency.
Demonstrated expertise in data visualization and analytics, with a keen eye for designing clear, compelling, and actionable insights that resonate with both technical and business audiences.
Experience working in GitHub, building and managing GCP resources using Pulumi and YAML configurations.
Strong SQL skills and experience working with large-scale datasets; proficiency in data modeling and dimensional design.
Proven experience analyzing structured and unstructured data using statistical methods, exploratory data analysis, and machine learning techniques to drive insights.
Proficiency in Python or similar languages for data processing and analytics.
Experience leveraging AI capabilities and tools (such as Vertex AI) to build machine learning models and AI solutions that enhance analytical capabilities.
Experience deploying and monitoring data solutions in production, with familiarity in CI/CD, version control, and cloud infrastructure best practices.
Strong ability to translate business questions into analytical frameworks and communicate findings effectively with both technical and non-technical stakeholders.

Responsibilities

Design and build robust data pipelines and architectures on Google Cloud Platform (GCP), leveraging BigQuery, Workflows, Cloud Scheduler, Dataproc, and Batch jobs.
Establish scalable ETL/ELT processes that efficiently ingest, transform, and prepare data from diverse sources for analytics and reporting.
Build and manage GCP resources using Pulumi and YAML configurations, and maintain code in GitHub.
Conduct in-depth exploratory data analysis and apply statistical methods, machine learning techniques, and AI-driven analytics to uncover patterns, trends, and actionable business insights.
Develop analytical models and leverage AI capabilities to support strategic decision-making and operational improvements across key business metrics.
Design and build intuitive, visually compelling dashboards and reports in Looker Studio that communicate complex data stories to diverse stakeholders.
Create data visualizations with a strong eye for clarity, aesthetics, and user experience that enable self-service analytics.
Leverage GCP's data ecosystem (BigQuery, Workflows, Cloud Scheduler, Dataproc, Batch, and Vertex AI) to optimize query performance, reduce costs, and enable real-time analytics.
Implement best practices for data governance, security, and scalability within GCP environments.
Work with business teams and stakeholders to understand data requirements and translate business questions into analytical solutions.
Document analytical findings and architectural decisions clearly, and mentor team members on data best practices.