Applied Machine Learning Scientist II; AI​/ML - Fraud​/Risk, GenAI & Agentic AI

Overview

The Applied Machine Learning Scientist II is responsible for providing technical knowledge and expertise on advanced analytics and machine learning across a broad range of analytics functions including data and modelling frameworks, tools, technology, processes and procedures. This role provides expertise in stakeholder interactions related to complex advanced analytics material and leads the development of AI/ML systems to solve complex problems, translating business objectives into technical solutions.

Department Overview:

The Advanced Analytics (AA) team at TD Bank serves as a Center of Excellence (CoE) delivering advanced analytics, Artificial Intelligence (AI), and Machine Learning (ML) solutions across U.S. business lines. The team partners with fraud, risk, operations, digital, and enterprise stakeholders to solve complex business challenges through data-driven innovation. AA develops scalable AI capabilities to improve operational efficiency, strengthen fraud and risk management, and enhance customer experiences.

The team leverages cloud-based technologies and AI methodologies including Generative AI, Agentic AI systems, machine learning, graph analytics, NLP, and predictive modeling to build intelligent solutions with measurable business impact. The organization fosters collaboration where scientists work with business leaders, engineers, MLOps, governance teams, and enterprise AI partners to product ionize AI technologies.

Position Overview:

We are seeking an experienced Applied Machine Learning Scientist to lead the development of next-generation AI/ML solutions focused on fraud, risk, operational intelligence, and decision optimization. The role suits a senior AI practitioner who combines deep technical expertise with strong business acumen and the ability to lead complex cross-functional initiatives from concept to production deployment. The candidate will advance the organization’s capabilities in Generative AI, Agentic AI, machine learning, and intelligent automation, and will mentor junior scientists while delivering scalable AI systems and working with senior business stakeholders to translate strategic priorities into deployable AI products.

Key Responsibilities

• Lead the end-to-end development and deployment of advanced AI/ML solutions addressing strategic business challenges across fraud, risk, and operational domains.
• Design and implement production-grade machine learning systems using advanced statistical modeling, deep learning, Generative AI, NLP, graph analytics, and Agentic AI frameworks.
• Drive innovation in emerging AI capabilities, including: LLM-powered applications; AI copilots and agentic workflows;
  Retrieval-Augmented Generation (RAG); multi-agent orchestration frameworks; intelligent decision support systems.
• Develop scalable data science and AI pipelines leveraging Python, Databricks, Azure, PySpark, MLflow, vector databases, orchestration frameworks, and modern AI tooling ecosystems.
• Partner with business leaders, fraud strategy teams, engineering, MLOps, governance, and enterprise AI organizations to identify opportunities and deliver measurable business value.
• Translate ambiguous business problems into analytical frameworks, technical solutions, and actionable insights.
• Lead technical architecture discussions and contribute to AI platform strategy, solution design, and enterprise AI standards.
• Communicate complex analytical concepts and AI solution designs effectively to executive leadership and stakeholders.
• Ensure model governance, explainability, monitoring, and responsible AI practices throughout the AI/ML lifecycle.
• Mentor and guide junior scientists by promoting best practices in machine learning, software engineering, experimentation, and AI product development.
• Stay aware of emerging industry trends and evolving AI technologies, proactively identifying opportunities to apply them within the organization.

Depth & Scope:

• Adept at technical project execution, strategic planning and effective communication; accountable for specialized knowledge in a field of AI/ML.
• Works autonomously and acts as a lead within a function.
• Undertakes complex initiatives requiring…