Applied Machine Learning Scientist II

TD•Montreal, QC

5d•CA$125,500 - CA$154,000•Onsite

About The Position

We're looking for a highly motivated Applied Machine Learning Scientist II to join our AI2 team. In this role, you'll apply expertise across the end-to-end AI and machine learning lifecycle, including model development, evaluation, testing, validation, deployment, and monitoring for both traditional machine learning and Generative AI solutions. You'll work closely with business, technology, risk, governance, and implementation partners to bring AI capabilities to life and deliver measurable business impact. This role offers an excellent opportunity to combine hands-on machine learning expertise with broader responsibilities related to AI solution assessment, vendor model evaluation, implementation, and governance. You will be expected to work with multiple business partners to advance the use of Machine Learning and AI at TD while supporting the responsible adoption of both internally developed and third-party AI solutions.

Requirements

Excellent written and verbal communication.
Comfortable and effective when interacting with a wide range of business partners and stakeholders.
Ability to develop and maintain strong internal relationships across business, technology, risk, and governance functions.
Ability to translate complex technical concepts and analytical findings into clear business language.
Creative, out-of-the-box thinker with strong conceptual and problem-solving skills.
Motivated to constantly identify innovative ways to enhance analytical solutions and AI implementation practices.
Capable of quickly identifying drivers of model performance variation, implementation risks, and monitoring concerns.
Ability to evaluate internally developed and vendor-provided AI solutions while balancing business value, performance, and governance requirements.
Proficiency in Python and modern machine learning frameworks and tools.
Experience developing, evaluating, and deploying machine learning and Generative AI solutions.
Strong understanding of model evaluation methodologies, experimentation, statistical testing, and performance monitoring.
Experience with structured and unstructured data, feature engineering, and model interpretability techniques.
Exposure to LLMs, agentic AI systems, and practical Generative AI applications.
Familiarity with model governance, Responsible AI principles, model validation, and model risk management practices.
Undergraduate degree in Science, Technology, Engineering, Mathematics, Economics, Finance, or a related quantitative discipline.
5+ years of relevant experience in machine learning, advanced analytics, data science, model evaluation, or related fields.

Nice To Haves

Graduate degree is considered an asset.
Experience with SQL, Azure Cloud, Azure ML Services, or Databricks is an asset.
Experience working in financial services or regulated environments.
Familiarity with model validation, model risk management, governance, or audit processes.
Experience evaluating vendor-provided analytical solutions, AI platforms, or commercial Generative AI products.
Familiarity with causal inference, anomaly detection, or agentic AI systems.

Responsibilities

Develop, deploy, and maintain Predictive and Generative AI solutions for use cases such as Agentic AI, LLM-based models, Pricing, and Anomaly Detection.
Lead the evaluation, implementation, testing, monitoring, and ongoing lifecycle management of both internally developed and third-party AI/ML solutions.
Assess vendor-provided and out-of-the-box AI models, including their capabilities, limitations, performance characteristics, implementation considerations, and governance implications.
Translate business problems into analytical frameworks and collaborate with cross-functional teams to define success metrics, testing methodologies, and solution approaches.
Conduct rigorous model evaluation, documentation, A/B testing, validation support, and monitoring to ensure model performance, fairness, stability, and compliance with Responsible AI principles.
Communicate complex technical results to technical and non-technical stakeholders and provide actionable recommendations regarding model performance, implementation, and risk.