Senior Manager of Machine Learning - Corporate Services

Senior Manager of Machine Learning – Corporate Services

Company	Vanguard
Location	Toronto, ON, Canada, Malvern, PA, USA
Salary	$Not Provided – $Not Provided
Type	Full-Time
Degrees	Master’s, PhD
Experience Level	Senior, Expert or higher

Master’s or PhD in Computer Science, Statistics, Machine Learning, Data Science, Electrical Engineering, or related field.
10+ years of experience in the machine learning space, with a minimum of 5 years directly managing engineering functions.
Proven ability to lead and develop high-performing ML teams, fostering a culture of innovation and continuous improvement.
Strong strategic thinking and problem-solving skills, focused on delivering business-impactful solutions.
Deep expertise in AI/ML frameworks and tools such as TensorFlow, PyTorch, Scikit-learn, and Keras. Proficiency in programming languages like Python, Java, or C++.
Proficiency in using AWS machine learning services, such as Sagemaker, for model pipeline development, training, and deployment. Proven ability to develop and deploy machine learning models with robust data architectures.
Experience with CI/CD practices for both machine learning and data engineering workflows.
Knowledge of data engineering principles and tools. Experience with data processing technologies, such as Apache Spark, AWS Glue, and Hadoop.
Proficiency in AWS Cloud Formation for infrastructure. Relevant certifications in AI/ML or cloud technologies.

Formulate and implement the AI/ML engineering strategy, ensuring it aligns with the corporate services’ overall business goals and technological vision.
Build, lead, and mentor a high-performing team of AI/ML engineers, fostering a collaborative and innovative environment.
Supervise the design, development, and deployment of AI/ML models and systems, ensuring they meet performance, scalability, and security standards.
Maintain a deep understanding of the latest AI/ML technologies and best practices and guide the team in their adoption and integration.
Design and implement robust infrastructure to support AI/ML workflows, including data pipelines, model training, and deployment environments.
Continuously optimize AI/ML models and systems for performance, efficiency, and cost-effectiveness.
Drive innovation by exploring new AI/ML techniques and technologies and integrating them into the organization’s processes and products.
Demonstrate thought leadership in the engineering space, with the ability to orchestration workstreams with cross-functional teams and stakeholders.