Associate Director for Data Strategy contributing to real - world evidence with advanced analytics and partnerships. Driving innovative projects in healthcare decision - making and data science in a collaborative environment.
About the role
- Lead the AI and machine learning initiatives at TELUS to drive data-driven decision making. Collaborate with various teams for innovative data solutions and insights.
Responsibilities
- Lead the iterative development, validation, and deployment of AI and machine learning models across the organization, ensuring continuous improvement and scalability
- Lead the development and deployment of generative AI applications, tools, and frameworks to solve critical problems
- Contribute to end-to-end automation efforts required for productionizing machine learning models, ensuring smooth deployment and operationalization
- Work with structured and unstructured data to design, develop, and deploy innovative predictive models, metrics, and dashboards that deliver actionable insights
- Present results creatively in various formats (e.g., dashboards, interactive reports) so insights are easily understood and actionable for stakeholders at all levels
- Influence strategic decision-making and promote organization-wide data-driven adoption by solving business challenges and uncovering new opportunities
- Develop reusable code and solutions to accelerate future goals and deliver results with increased speed and reliability
- Support the evolution of data science and AI solutions by influencing product roadmaps, prioritizing features that meet business needs, and recommending the latest research, tools, and industry best practices
- Build and maintain strong engagement with key stakeholders, understand their priorities and business needs, and present AI and machine learning initiatives to vice-presidential and senior leadership
- Act as a functional leader for AI and machine learning across the enterprise, provide technical leadership for the overall AI and ML program, and collaborate with other business units to deploy AI and ML solutions
- Establish best practices for deploying and operating machine learning models, ensuring models are scalable, reliable, and optimized for long-term success
- Represent the company and position it as a leading authority in AI and machine learning by presenting at conferences, publishing articles, and participating in external forums to build TELUS’s reputation as a global leader in data science and AI
- Provide coaching and mentorship to a growing team of engineers and data scientists, foster a culture of continuous learning and innovation, and identify future leaders within the organization
Requirements
- Minimum of 7 to 10 years of hands-on experience in machine learning, AI, and data analytics, including deploying solutions into operational processes
- Strong expertise with Python and experience using data science libraries (e.g., scikit-learn, Pandas, NumPy)
- Deep experience with machine learning algorithms, including regression, classification, clustering, time series analysis, reinforcement learning, and optimization
- Experience designing and deploying generative AI applications and processes
- Proficiency in SQL and distributed computing
- Hands-on experience with cloud platforms such as GCP, AWS, or Azure
- Expertise using machine learning frameworks like TensorFlow, PyTorch, and Keras
- Solid understanding of software and AI development lifecycles, and experience with MLOps and development/operations practices
- Familiarity with version control systems (e.g., Git) for collaborative development
- Ability to effectively communicate complex technical concepts to non-technical audiences
- Dynamic, agile approach to remove roadblocks and deliver results quickly
Benefits
- Comprehensive total compensation package highlighting competitive salary and bonus structures
- Minimum of 3 weeks of vacation
- Flexible benefits plan to meet the needs of you and your family
- Retirement plan and employee share ownership program with generous employer contributions
- Option to work in-office, virtually, or a hybrid schedule depending on role requirements
- Opportunities to give back to the communities where we live and work
- Career growth, learning, and development opportunities
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