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, orchestration frameworks, and modern AI tooling ecosystems.
• Partner closely 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, business stakeholders, and governance partners.
• Ensure strong 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.
• Maintain awareness of emerging industry trends, academic research, 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 accountable for acting as a lead within a function
• Undertakes and completes a variety of complex initiatives requiring seasoned specialist AI/ML knowledge and/or the integration of cross functional processes
• Typically deals with senior/executive management

Education & Experience:

• Undergraduate degree required, advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience, or;
• 1+ years relevant experience (includes post graduate experience)

Preferred Qualifications:

• Advanced degree (Master's or PhD preferred) in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or related quantitative discipline.
• Extensive experience developing and deploying advanced AI/ML solutions in enterprise environments.
• Strong hands-on experience with machine learning, deep learning, NLP, LLMs, Generative AI, and modern AI application architectures.
• Experience building or implementing:
  * Agentic AI systems
  * AI copilots
  * Retrieval-Augmented Generation (RAG)
  * Prompt engineering frameworks
  * Multi-agent workflows
  * Conversational AI solutions
  * Knowledge retrieval systems
• Deep expertise in Python, PySpark, SQL, and modern ML/AI frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, Hugging Face, MLflow, or equivalent ecosystems.
• Experience working with cloud-based AI/ML platforms such as Azure Databricks and distributed computing environments.
• Strong software engineering and productionization skills, including Git-based development workflows, CI/CD concepts, API integration, and scalable AI solution deployment.
• Experience developing AI/ML solutions within fraud, financial services, risk, payments, or highly regulated industries is strongly preferred.
• Strong understanding of AI governance, explainability, model risk management, and responsible AI principles.
• Demonstrated ability to lead complex initiatives and influence cross-functional stakeholders in fast-paced enterprise environments.
• Excellent communication and presentation skills with the ability to explain technical concepts to both technical and non-technical audiences.
• Proven ability to mentor junior team members and contribute to a strong collaborative team culture.
• Strong research mindset with the ability to evaluate and operationalize emerging AI techniques from academic and industry research.

Customer Accountabilities:

• Provides strong knowledge in a given AI/ML area. Delivers insights into leading analytic practices, designs and leads iterative learning and development cycles, and ultimately produces new and creative analytic solutions that will become core deliverables
• Works closely with business owners to identify opportunities and serves as an ambassador for data science
• Designs and delivers enterprise analytic solutions for customers
• Develops powerful business insights from a broad range of data using advanced machine learning techniques
• Works in a highly interactive, team-oriented environment with Big Data developers, engineers, modelers and others

Shareholder Accountabilities:

• Adheres to enterprise frameworks or methodologies that relates to activities for our business area
• Ensures respective programs/policies/practices are well managed, meets business needs, complies with internal and external requirements, and aligns with business priorities
• Consistently exercises discretion in managing correspondence, information and all matters of confidentiality; escalates issues where appropriate
• Ensures business operations are in compliance with applicable internal and external requirements (e.g. financial controls, segregation of duties, transaction approvals and physical control of assets)
• Participates in cross-functional/enterprise initiatives as a subject matter expert helping to identify risk/provide guidance for complex situations
• Conducts internal and external research projects; supports the development/delivery of presentations/communications to management or broader audience
• Conducts meaningful analysis at the functional or enterprise level using results to draw conclusions, makes recommendations, assesses the effectiveness of programs/policies/practices
• Monitors service, productivity and assesses efficiency levels within own function and implements continuous process/performance improvements where opportunities exist
• Leads/facilitates and/or implements action/remediation plans to address performance/risk/governance issues
• Actively manages relationships within and across various business lines, corporate and/or control functions and ensures alignment with enterprise and/or regulatory requirements
• Keeps abreast of emerging issues, trends, and evolving regulatory requirements and assesses potential impacts
• Maintains a culture of risk management and control, supported by effective processes in alignment with risk appetite

Employee/Team Accountabilities:

• Participates fully as a member of the team, supports a positive work environment that promotes service to the business, quality, innovation and teamwork and ensures timely communication of issues/points of interest
• Provides thought leadership and/or industry knowledge for own area of expertise in own area and participates in knowledge transfer within the team and business unit
• Keeps current on emerging trends/developments and grows knowledge of the business, related tools and techniques
• Participates in personal performance management and development activities, including cross training within own team
• Keeps others informed and up to date about the status/progress of projects and/or all relevant or useful information related to day-to-day activities
• Contributes to team development of skills and capabilities through mentorship of others, by sharing knowledge and experiences and leveraging best practices
• Leads, motivates and develops relationships with internal and external business partners/stakeholders to develop productive working relationships. Contributes to a fair, positive and equitable environment that supports a diverse workforce
• Acts as a brand ambassador for your business area/function and the bank, both internally and/or externally

Physical Requirements:

Never: 0%; Occasional: 1-33%; Frequent: 34-66%; Continuous: 67-100%

• Domestic Travel - Occasional
• International Travel - Never
• Performing sedentary work - Continuous
• Performing multiple tasks - Continuous
• Operating standard office equipment - Continuous
• Responding quickly to sounds - Occasional
• Sitting - Continuous
• Standing - Occasional
• Walking - Occasional
• Moving safely in confined spaces - Occasional
• Lifting/Carrying (under 25 lbs.) - Occasional
• Lifting/Carrying (over 25 lbs.) - Never
• Squatting - Occasional
• Bending - Occasional
• Kneeling - Never
• Crawling - Never
• Climbing - Never
• Reaching overhead - Never
• Reaching forward - Occasional
• Pushing - Never
• Pulling - Never
• Twisting - Never
• Concentrating for long periods of time - Continuous
• Applying common sense to deal with problems involving standardized situations - Continuous
• Reading, writing and comprehending instructions - Continuous
• Adding, subtracting, multiplying and dividing - Continuous

The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties and skills required. The listed or specified responsibilities & duties are considered essential functions for ADA purposes.