Data Scientist

Description

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 CSD AI Innovation Hub within Customer Solutions Delivery (CSD). We are a dynamic and agile team revolutionizing field operations by designing data-driven AI solutions that optimize OpEx, CX, sales, billing, and other key metrics across our national technician workforce. Our AI Hub is the go-to destination for autonomous, creative professionals passionate about developing their talents while solving some of TELUS' most significant challenges.

What you’ll do

As a Data Scientist, you’ll work closely with stakeholders and software engineers to identify and design high-impact AI solutions, including RAG/Agentic-based applications, ETL pipelines that leverage LLMs to extract key insights/enhance data, and much more.

Your Responsibilities:

• Data Analysis: Leverage traditional machine learning, NLP techniques, and Large Language Models (LLMs) to analyze notes, transcripts and other unstructured text data. Develop models for tasks such as topic modeling, classification, sentiment analysis, entity extraction, and error detection. Establish robust evaluation frameworks (precision, recall, F1) and conduct iterative error analysis to continuously improve performance and reliability.
• LLM App and API Development: Design and build LLM-powered applications and services to assist technicians. Apply best practices in prompt engineering (Chain-of-Thought, few-shot prompting, structured outputs), Retrieval-Augmented Generation (RAG), and agentic systems (tool usage, multi-step reasoning, API chaining, stateful workflows). Implement guardrails, validation layers, and hallucination mitigation strategies.\
• System Design and Implementation: Architect, develop, and deploy scalable AI-powered APIs, applications and automated workflows. Make deliberate design tradeoffs balancing latency, cost, performance, reliability, and lifecycle ownership. Build modular, maintainable systems that integrate seamlessly with enterprise data sources and operational platforms.
• MLOPs and Deployment: Implement CI/CD pipelines for deployments. Manage infrastructure using Infrastructure as Code (IaC), containerize services, and deploy to Kubernetes. Establish monitoring, logging, versioning, and rollback strategies to ensure reliability, observability, and scalability in production.
• Collaboration and Mentoring: Partner with engineering, operations, and business stakeholders to translate real-world challenges into AI solutions. Clearly document architectural decisions and learnings. Mentor team members and contribute to a culture of technical excellence.

Advanced knowledge of English is required because you will most of the time interact in English with internal parties (colleagues, internal partners, stakeholders, etc.); and work with IT tools whose interface is only accessible in English as part of this position's main responsibilities given its national scope.

Qualifications

What you bring

• Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative discipline — or a PhD in a relevant field.
• 3+ years of experience applying machine learning and AI in production environments, delivering measurable business impact.
• Strong foundation in traditional ML and NLP (classification, regression, clustering, topic modeling, sentiment analysis) with experience designing robust evaluation frameworks (precision/recall/F1, experimentation, error analysis).
• Practical experience working with LLMs, including prompt engineering, structured outputs, and building Retrieval-Augmented Generation (RAG) systems.
• Experience designing and implementing agentic AI systems, including multi-step reasoning workflows, tool/API orchestration, memory/state management, and production guardrails.
• Experience building AI-powered applications and APIs, with an understanding of latency, scalability, cost optimization, and reliability tradeoffs.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and experience developing and consuming REST APIs.
• Experience building data pipelines (ETL/ELT), preprocessing structured and unstructured data, and strong SQL skills working with large-scale datasets.
• Experience deploying models using Docker and Kubernetes, with familiarity in CI/CD, cloud platforms (GCP) and production monitoring.
• Strong ability to translate ambiguous business problems into scalable AI systems and communicate effectively with both technical and non-technical stakeholders.