Applied Machine Learning Scientist II (AI/ML - Fraud/Risk, GenAI & Agentic AI)

Work Location:

New York, New York, United States of America

Hours:

40

Pay Details:

$96,130 - $155,950 USD

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.

Line of Business:

Analytics, Insights, & Artificial Intelligence

Job Description:

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 generally provides expertise in stakeholder interactions related to complex advanced analytics related material. Additionally, this role plays a lead role in the development of AI/ML systems to solve a range of complex problems and is adept at 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 closely with fraud, risk, operations, digital, and enterprise stakeholders to solve complex business challenges through data-driven innovation.

AA is at the forefront of developing scalable AI capabilities that improve operational efficiency, strengthen fraud and risk management, and enhance customer experiences. The team leverages modern cloud-based technologies and advanced AI methodologies — including Generative AI, Agentic AI systems, machine learning, graph analytics, NLP, and predictive modeling — to build intelligent solutions that create measurable business impact.

The organization fosters a highly collaborative and innovative environment where scientists work closely with business leaders, engineers, MLOps, governance teams, and enterprise AI partners to transform emerging AI technologies into production-ready enterprise solutions.

Position Overview:

We are seeking a highly experienced and technically strong Applied Machine Learning Scientist to lead the development of next-generation AI/ML solutions focused on fraud, risk, operational intelligence, and decision optimization.

This role is ideal for a senior AI practitioner who combines deep technical expertise with strong business acumen and the ability to lead complex cross-functional initiatives from concept through production deployment. The successful candidate will play a key role in advancing the organization’s capabilities in Generative AI, Agentic AI, machine learning, and intelligent automation.

The role requires hands-on expertise in building scalable AI systems while also serving as a technical leader and mentor for junior scientists. Candidates should be comfortable operating in highly ambiguous problem spaces, rapidly prototyping innovative solutions, and collaborating directly with senior business stakeholders to translate strategic priorities into deployable AI products.

Key Responsibilities include:

• 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
• *Human-in-the-loop AI solutions
• Develop scalable data science and AI pipelines leveraging technologies such as Python, Databricks, Azure, PySpark, MLflow, vector databases, o…