Data Scientist

Ontario - Canada

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

Description

Be a part of a transformational journey with innovative talent and leading edge technologies.

Join our team and what well 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 youll do

As a Data Scientist youll 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 positions main responsibilities given its national scope.

Qualifications

What you bring

Masters 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.

Required Experience:

Description Be a part of a transformational journey with innovative talent and leading edge technologies.Join our team and what well accomplish togetherThis is an exciting opportunity to join the CSD AI Innovation Hub within Customer Solutions Delivery (CSD). We are a dynamic and agile team revoluti...