AVP, AI & Innovation

TD • New York, NY

1d • $200,000 - $280,000 • Onsite

About The Position

Lead TD Securities’ Applied AI and Innovation capability and build a high-performing team of applied AI specialists. Identify relevant advances in AI and translate them into practical capabilities that can be tested, proven, and deployed in capital markets workflows. This business-led senior technical leadership role combines deep AI expertise, product judgment, and pragmatic delivery, connecting emerging models, methods, and research with complex business problems across business, quantitative, data, engineering, and control teams.

Set the applied AI strategy, technical direction, and priorities across areas including generative AI, agentic systems, decision intelligence, multimodal models, retrieval, reasoning, and human-agent operating models. Identify high-value opportunities and lead the development and evaluation of AI solutions, reusable components, standards, and product capabilities. Ensure validated prototypes can be engineered, governed, scaled, monitored, and supported in production.

Build and develop a multidisciplinary applied AI team, lead through technical credibility, and partner across front office, data, technology, architecture, risk, compliance, legal, information security, finance, operations, change management, and delivery teams. Ensure AI adoption is responsible, controlled, auditable, and explainable, with clear human accountability for material decisions. The role is generally not directly client-facing; it creates client and shareholder value by improving front-office service, opportunity identification, decision-making, risk management, and execution.

Requirements

• 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, including 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.
• Executive communication skills to explain technical findings, uncertainty, limitations, and investment choices precisely without oversimplification.

Nice To Haves

• 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.
• Capital markets or financial-services experience strongly preferred.
• Experience with market, client, transaction, research, risk, or unstructured financial data is advantageous.

Responsibilities

• Lead the Applied AI and Innovation capability for TD Securities and build a high-performing team of applied AI specialists.
• Set applied AI strategy, technical direction, and priorities across generative AI, agentic systems, decision intelligence, multimodal models, retrieval, reasoning, and human-agent operating models.
• Assess research and industry developments for practical relevance and translate promising capabilities into experiments, product strategies, operating models, and value opportunities.
• Determine where advanced AI offers a material advantage over conventional analytics, rules, or automation.
• 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 performance, latency, privacy, explainability, control, or cost requirements.
• Drive rapid prototyping and ensure successful solutions can be engineered, governed, scaled, monitored, and supported in production.
• 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 and address 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.
• 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.
• Apply product-management disciplines to AI capabilities, including vision, roadmaps, lifecycle management, prioritization, adoption, and value tracking.
• Work 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.
• 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.
• 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.
• 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 appropriate accountable partners.
• Coordinate with front-office leaders on opportunity prioritization, workflow design, commercial impact, adoption, and value realization; and with partner teams on data, technology, risk, finance, operations, change, and delivery.
• Collaborate with approved universities, research institutes, vendors, and open-source communities for collaboration, talent pipelines, and knowledge exchange.
• Deliver measurable value, approved roadmap milestones, adoption and outcome improvements, reliable production performance, reusable AI capabilities, team growth, and cross-functional delivery effectiveness.

Benefits

• 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
• Reward and recognition
• Variable compensation/incentive awards, including potential eligibility for cash and/or equity incentive awards

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree