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About the Role
We are seeking a motivated Applied Machine Learning Scientist I to join our TDI AI/ML team, focusing on Generative AI, LLM-based and agentic solutions, with opportunities in predictive machine learning. You will contribute to the end-to-end AI and machine learning lifecycle, collaborating with various partners to deliver impactful AI capabilities.
Responsibilities
- Contribute to the development, deployment, and maintenance of Generative AI and predictive machine learning solutions for use cases such as customer and employee assistance, claims and underwriting support, operational automation, and risk assessment.
- Support the evaluation, implementation, testing, monitoring, and ongoing lifecycle management of both internally developed and third-party AI/ML solutions.
- Assist in assessing vendor-provided and out-of-the-box AI models, including their capabilities, limitations, performance characteristics, implementation considerations, and governance implications.
- Help translate well-defined business problems into analytical frameworks and collaborate with cross-functional teams to define success metrics, testing methodologies, and solution approaches.
- Contribute to model evaluation, documentation, A/B testing, validation support, and monitoring to help ensure model performance, fairness, stability, and compliance with Responsible AI principles.
- Communicate technical results clearly to technical and non-technical stakeholders and contribute to recommendations regarding model performance, implementation, and risk.
Requirements
- Excellent written and verbal communication skills.
- Comfortable collaborating with a range of business partners and stakeholders.
- Ability to build positive working 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.
- Ability to investigate drivers of model performance variation and identify potential implementation or monitoring concerns with guidance.
- Interest in evaluating internally developed and vendor-provided AI solutions while considering business value, performance, and governance requirements.
- Proficiency in Python and modern machine learning frameworks and tools.
- Practical experience developing or evaluating machine learning or Generative AI solutions through professional work, internships, research, or substantial academic or personal projects.
- Understanding of model evaluation methodologies, experimentation, statistical testing, and performance monitoring.
- Familiarity with structured and unstructured data, feature engineering, and model interpretability techniques.
- Exposure to LLMs, agentic AI systems, and practical Generative AI applications.
- Familiarity with agent orchestration frameworks such as LangGraph, gained through work, research, coursework, or personal projects.
- Understanding of stateful, multi-step agent workflows involving deterministic logic, LLM-driven reasoning, tool calling, conditional routing, memory, or human-in-the-loop controls.
- Exposure to integrating LLM applications with APIs, retrieval-augmented generation pipelines, vector stores, structured outputs, or external tools and data sources.
- Familiarity with production practices for Generative AI systems, such as prompt and workflow versioning, automated evaluation, tracing, observability, guardrails, retry and fallback strategies, or latency and cost considerations.
- Familiarity with model governance, Responsible AI principles, model validation, or model risk management practices is an asset.
- Experience with SQL, Azure Cloud, Azure ML Services, or Databricks is an asset.
Skills
- Generative AI
- LLM-based solutions
- Agentic solutions
- Predictive machine learning
- Python
- Machine learning frameworks
- Model evaluation
- Experimentation
- Statistical testing
- Performance monitoring
- Structured data
- Unstructured data
- Feature engineering
- Model interpretability
- LLMs
- Agentic AI systems
- Agent orchestration frameworks
- LangGraph
- API integration
- Retrieval-augmented generation
- Vector stores
- Prompt versioning
- Workflow versioning
- Automated evaluation
- Tracing
- Observability
- Guardrails
- Retry strategies
- Fallback strategies
- Latency considerations
- Cost considerations
- Model governance
- Responsible AI principles
- Model validation
- Model risk management
- SQL
- Azure Cloud
- Azure ML Services
- Databricks
Location
- Toronto, Ontario, Canada
Work Type
- Full-time
Experience Level
- 1–3 years of relevant experience in machine learning, advanced analytics, data science, model evaluation, or related fields. Relevant internships, research, co-op placements, and substantial academic or personal projects may be considered.
Education Level
- Undergraduate degree in Science, Technology, Engineering, Mathematics, Economics, Finance, or a related quantitative discipline.
- Graduate degree is considered an asset.
Salary/Compensations
- 105,500 - 125,000 CAD
Benefits
- Health and well-being benefits
- Savings and retirement programs
- Paid time off
- Banking benefits and discounts
- Career development
- Reward and recognition programs
- Training programs
- Competitive benefits plan
About the Company
- TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores.
- We strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world.
- More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience.
- TD is deeply committed to being a leader in client experience.
- We believe that all colleagues, no matter where they work, are client facing.
- Together, we are reimagining what banking can be for our clients, colleagues and communities.
Equal Opportunity
- We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals.
- Here at TD, we hire and develop the best.