Applied Machine Learning Scientist I

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

We're looking for a highly motivated Applied Machine Learning Scientist I to join our TDI AI/ML team. This role is primarily focused on Generative AI, including LLM-based and agentic solutions, while also offering opportunities to work on predictive machine learning use cases. You'll contribute across the end-to-end AI and machine learning lifecycle, including solution design, model development, evaluation, testing, validation, deployment, monitoring, and continuous improvement. You'll collaborate with business, technology, risk, governance, and implementation partners to help bring AI capabilities to life with measurable business impact.

This role offers an excellent opportunity to build hands-on machine learning expertise while gaining exposure to AI solution assessment, vendor model evaluation, implementation, and governance. You will work with business partners and experienced colleagues to advance the use of AI and Machine Learning at TDI while supporting the responsible adoption of both internally developed and third-party AI solutions.

• 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.

If you’re interested in a specific career path or are looking to build certain skills, we want to help you succeed. You’ll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.

If you’re passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.  

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.

• Excellent written and verbal communication skills.
• Comfortable collaborating with a range of business partners and stakeholders, with a willingness to develop these skills further.
• 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.

• Undergraduate degree in Science, Technology, Engineering, Mathematics, Economics, Finance, or a related quantitative discipline.
• Graduate degree is considered an asset.
• 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.

TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, 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, that is why 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.