AVP, AI & Innovation

Work Location:

New York, New York, United States of America

Hours:

40

Line of Business:

TD Securities

Pay Detail:

$200,000 - $280,000 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.

Job Description:

Role mandate

Lead the Applied AI and Innovation capability for TD Securities and build a high-performing team of applied AI specialists. Identify advances in AI that matter to the business and translate them into practical capabilities that can be tested, proven, and deployed in capital markets workflows.

This is a business-led senior technical leadership role requiring deep AI expertise, strong product judgment, and a pragmatic delivery mindset. The role connects emerging models, methods, and research with complex business problems and works across business, quantitative, data, engineering, and control teams to deliver measurable value.

Strategy and technical direction

• Set the applied AI strategy, technical direction, and priorities across generative AI, agentic systems, decision intelligence, multimodal models, retrieval, reasoning, and human-agent operating models for TD Securities.
• Assess research and industry developments in generative AI, agentic AI, machine learning, retrieval, reasoning, multimodal models, and related technologies, for practical relevance, then translate the most promising capabilities into experiments, product strategies, operating models, and value opportunities.
• Determine where advanced AI provides a material advantage over conventional analytics, rules, or automation, avoiding unnecessary complexity.

Applied AI delivery and value creation

• Identify and prioritize high-value opportunities; develop business cases, value hypotheses, adoption plans, measurable baselines, and value-realization frameworks.
• Lead applied scientists in designing, building, testing, and improving AI solutions for complex, knowledge-intensive and analytical workflows.
• Establish standards for experimentation, benchmarking, statistical rigor, error analysis, robustness, documentation, reproducibility, and production readiness.
• Develop or adapt models and methods when existing solutions do not meet requirements for performance, latency, privacy, explainability, control, or cost.
• Drive rapid prototyping while ensuring successful solutions can be engineered, governed, scaled, monitored, and supported in production.

Technical leadership

• Provide senior technical oversight across model selection and adaptation, fine-tuning, retrieval-augmented generation, agent orchestration, knowledge and semantic methods, tool use, planning, memory, context management, hybrid retrieval, reranking, evaluation, and inference optimization.
• Design robust agentic and retrieval systems, spanning tool use, planning, memory, context management, hybrid retrieval and reranking, while proactively addressing scaling, data quality, evaluation blind spots and inference cost.
• Set rigorous experimentation and evaluation standards, including controlled ablations, multi-seed testing, statistical significance, red teaming and contamination controls to ensure solutions are reliable and production-ready.
• Anticipate and mitigate scaling bottlenecks, evaluation blind spots, data quality issues, model risk, security concerns, and inference cost.
• Challenge architecture and model choices; lead design reviews and resolve significant trade-offs with Engineering, Data, Architecture, Technology, and control partners.

AI products and capability development

• Apply product-management disciplines to AI capabilities, including vision, roadmaps, lifecycle management, prioritization, adoption, and value tracking.
• Work directly with quantitative practitioners, traders, bankers, risk specialists, data scientists, and engineers to frame problems and design practical AI solutions.
• Establish reusable AI components, evaluation frameworks, implementation patterns, and technical standards that accelerate delivery across teams.
• Apply AI to information extraction and synthesis, decision support, workflow automation, quantitative and analytical tasks, client intelligence, risk analysis, and other complex processes.
• Partner with engineering and delivery teams to move validated prototypes into reliable production capabilities.

People and capability leadership

• Build, lead, and develop a multidisciplinary applied AI team with a high bar for technical quality, scientific rigor, engineering discipline, and measurable impact.
• Lead through technical credibility by authoring design documents, running architecture and technical reviews, and setting expectations for code, experiments, and system quality.

 

Governance, risk, and responsible adoption

• Partner with Risk, Compliance, Legal, Information Security, Technology, and enterprise AI governance teams to ensure responsible, controlled, auditable, and explainable adoption.
• Define guardrails, escalation paths, adoption controls, measurement routines, and governance artifacts for AI products and agentic workflows, while preserving clear human accountability for material decisions.

Decision authorities

• Prioritize the AI product and transformation portfolio with front-office sponsors and enabling partners.
• Determine which emerging capabilities warrant experimentation and which prototypes have sufficient evidence to progress toward production.
• Recommend investments in models, tooling, platforms, compute, talent, and external partnerships.
• Escalate material risk, control, data, compliance, or technology dependencies to the appropriate accountable partners.

The role shares responsibilities and decision interfaces with:

• Front Office leaders for opportunity prioritization, workflow design, commercial impact, adoption, and value realization.
• Data Product teams for reusable, business-ready data; semantic context; quality; lineage; provenance; metadata; and AI-ready information structures.
• Technology and Architecture for secure implementation, integration, platform alignment, resiliency, production readiness, and sustainable support.
• Risk, Compliance, Legal, Information Security, and enterprise AI Governance for control design, policy alignment, approvals, responsible AI, and auditability.
• Finance and Business Management for business cases, budgets, benefits tracking, operating leverage, and value realization.
• Operations, Change Management, Human Resources, and Learning for workflow change, adoption, training, role impacts, and capability building.
• Product and Delivery partners for execution planning, backlog prioritization, stakeholder engagement, success metrics, and delivery discipline.
• Approved universities, research institutes, vendors, and open-source communities for collaboration, talent pipelines, and knowledge exchange.

• Key measures will balance technical quality, adoption, and realized business impact:
• Realized value from AI-enabled products, capabilities, and workflow transformation, including productivity, decision quality, revenue support, risk insight, or control effectiveness.
• Delivery of approved roadmap milestones, business cases, benefits plans, and strategic transformation objectives.
• Adoption, outcome improvement, reliability, and production performance of deployed AI capabilities.
• Number, maturity, and reuse of AI components, evaluation frameworks, implementation patterns, workflows, and skills.
• Team growth, talent development, stakeholder confidence, and cross-functional delivery effectiveness.
• Portfolio, project, cost-centre, and value-realization targets to be confirmed.

• This role is generally not directly client-facing. It creates client and shareholder value by improving how front-office teams serve clients, identify opportunities, make decisions, manage risk, and execute work.
• Improves the speed, quality, and consistency of client coverage through AI-enabled insights, workflow tools, and decision support.
• Reduces manual effort and accelerates access to relevant business context and information.
• Supports revenue generation and relationship management by helping teams prepare more effectively and act on insights faster.
• Simplifies fragmented workflows and embeds intelligence into day-to-day processes.
• Strengthens control, transparency, and consistency through governed, explainable, reusable AI capabilities.

Education

PhD strongly preferred in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Operations Research, Engineering, Physics, or a related quantitative discipline. A research-intensive master’s degree may be considered with an exceptional record of applied technical leadership.

Experience and technical capabilities

• Typically 8+ years of progressive experience in applied AI, machine learning, data science, quantitative research, or advanced analytics, including leadership of multidisciplinary technical teams.
• Demonstrated success translating research into robust, production-used systems in complex, regulated, or high-consequence environments.
• Deep expertise in several areas such as large language models, natural language processing, retrieval and reasoning, agentic systems, multimodal learning, graph methods, time-series modelling, causal inference, optimization, reinforcement learning, or probabilistic modelling.
• Expert command of experimental design, statistical inference, benchmarking, ablation studies, error analysis, model evaluation, reproducibility, contamination controls, and scientific peer review.
• Strong hands-on fluency in Python and modern AI/ML ecosystems, with the ability to review code, experiments, model behaviour, architecture, and data pipelines.
• Strong understanding of production AI architecture, including feature and data pipelines, vector and graph retrieval, model serving, APIs, distributed compute, cloud platforms, MLOps and LLMOps, observability, security, latency, resiliency, and cost optimization.
• Technical depth to challenge designs, assess feasibility and risk, resolve architecture trade-offs, and partner credibly with data, engineering, quantitative, and technology teams.
• Experience building and developing applied science or research teams, including talent assessment, mentoring, organizational design, and capability building.
• Ability to engage credibly with quantitative practitioners, engineers, data scientists, risk specialists, traders, bankers, and senior technology leaders.
• Capital markets or financial-services experience strongly preferred; experience with market, client, transaction, research, risk, or unstructured financial data is advantageous.
• Executive communication skills to explain technical findings, uncertainty, limitations, and investment choices precisely without oversimplification.

Who We Are

TD Securities offers a wide range of capital markets products and services to corporate, government, and institutional clients who choose us for our innovation, execution, and experience. With more than 6,500 professionals operating out of 40 cities across the globe, we strive to make every interaction, product and experience remarkably human and refreshingly simple.  Our services include underwriting and distributing new issues, providing trusted advice and industry-leading insight, extending access to global markets, and delivering integrated transaction banking solutions. In 2023, we acquired Cowen Inc., offering our clients access to a premier U.S. equities business and highly-diverse equity research franchise, while growing our strong, diversified investment bank. 

Together, we are reimagining what banking can be for our clients, colleagues and communities. 

Our Total Rewards Package
Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical and mental well-being goals. Total Rewards at TD includes base salary and variable compensation/incentive awards (e.g., eligibility for cash and/or equity incentive awards, generally through participation in an incentive plan) and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off (including Vacation PTO, Flex PTO, and Holiday PTO), banking benefits and discounts, career development, and reward and recognition. Learn more
 

Additional Information:
We’re delighted that you’re considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we’re committed to providing the support our colleagues need to thrive both at work and at home.

Colleague Development

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.
 

Training & Onboarding
We will provide training and onboarding sessions to ensure that you’ve got everything you need to succeed in your new role.

Interview Process
We’ll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call.

Accommodation

TD Bank is an equal opportunity employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, status as a protected veteran or any other characteristic protected under applicable federal, state, or local law.

If you are an applicant with a disability and need accommodations to complete the application process, please email TD Bank US Workplace Accommodations Program at [email protected].  Include your full name, best way to reach you and the accommodation needed to assist you with the applicant process.