Role Summary
Leads a multidisciplinary team of data scientists, ML engineers and applied AI practitioners delivering diagnostic, predictive, prescriptive and generative AI capabilities for the Advice and Wealth Management business. Owns the end-to-end lifecycle — from problem framing and hypothesis design through model and agent development, evaluation, deployment, monitoring and decommissioning — within an enterprise responsible AI and model risk governance framework. Balances people leadership with deep personal technical engagement, and operates as a trusted advisor to business, product, compliance and risk stakeholders.
Core Responsibilities:
Leadership and Talent
- Builds and leads the team. Hires, develops, evaluates and supervises crew across data science, ML engineering and applied AI disciplines. Sets performance standards, conducts reviews and makes informed compensation decisions in accordance with applicable Human Resources policies and procedures.
- Develops modern AI capability. Establishes structured upskilling in generative AI, prompt and context engineering, agentic patterns, retrieval architectures and evaluation methodology. Coaches crew on when a classical model, a fine-tuned model, a retrieval pipeline or an agentic workflow is the appropriate solution — and when AI is not the answer at all.
- Shapes strategy. Translates enterprise AI and data science strategy into a prioritized, sequenced tactical roadmap for AWM. Builds the business case for investment, articulating expected value, delivery risk, run cost and time to impact for senior leadership.
Generative and Agentic AI Delivery
- Owns agentic design and architecture. Directs the design of multi-agent and orchestrated systems — planner/executor patterns, tool and function calling, memory and state management, retrieval-augmented generation, human-in-the-loop checkpoints, and deterministic fallback paths. Sets standards for agent decomposition, inter-agent contracts, failure handling and escalation to a human advisor.
- Directs model selection and the model portfolio. Evaluates and selects across frontier, open-weight, small language and domain-tuned models. Owns the build-versus-buy-versus-tune decision, and applies fine-tuning, distillation, prompt optimization and retrieval strategies based on measured accuracy, latency, reliability and cost trade-offs.
- Manages the token economy and unit economics. Owns cost-per-task, cost-per-interaction and cost-per-served-client as first-class engineering metrics. Drives token efficiency through context compression, caching, model routing and right-sizing, batching and tiered inference. Forecasts consumption, manages capacity and quota, and reports unit economics and ROI to finance and technology leadership.
- Establishes evaluation as a discipline. Builds and maintains rubric-based, golden-dataset and LLM-as-judge evaluation harnesses covering accuracy, groundedness, hallucination rate, tone, refusal behavior, latency and regression across releases. Ensures no generative or agentic capability reaches production without a quantitative, repeatable evaluation baseline and ongoing drift monitoring.
Analytics, Modeling and Quality
- Oversees the full analytics portfolio. Manages a portfolio spanning statistical and machine learning models, causal inference and experimentation, forecasting, and generative and agentic applications. Engages personally in deep-dive analysis and creates alternative model approaches to stress complex design decisions and advance future capability.
- Applies rigorous quality control and risk assessment. Leads validation of models, methodologies, data, prompts, agent trajectories and outputs for major initiatives. Leads and coaches test design, research design, experimental design and model validation, and provides statistical consultation across the organization.
- Governs data quality and provenance. Identifies and diagnoses data inconsistencies and errors, documents data assumptions, and forages to fill data gaps. Extends this discipline to unstructured and retrieval corpora — source authority, freshness, chunking strategy, embedding quality, lineage and permissions-aware access.
- Ensures production reliability. Partners with engineering on MLOps and LLMOps — CI/CD for models and prompts, versioning, observability and tracing, guardrail enforcement, incident response and rollback. Owns operational SLAs for deployed capabilities.
Governance, Risk and Responsible AI
- Operationalizes responsible AI. Implements enterprise AI governance across the model and agent lifecycle — intake and use-case triage, risk tiering, documentation, approval gates, and periodic recertification. Embeds guardrails for safety, privacy, fairness, disclosure, explainability and appropriate human oversight.
- Navigates a regulated advice environment. Partners with Legal, Compliance, Risk and Model Risk Management to ensure capabilities meet fiduciary, suitability, supervision, recordkeeping and disclosure obligations applicable to advice and wealth management. Ensures AI-assisted guidance is traceable, reviewable and defensible.
- Manages third parties and emerging technology. Evaluates new technologies, models and platforms; manages vendors and model providers engaged in AI and large-scale data work. Assesses concentration risk, data handling terms, roadmap viability and total cost of ownership.
Stakeholder Engagement
- Partners with the business. Engages internal stakeholders to understand and probe business processes, brings structure to ambiguous requests, and translates requirements into an analytic or AI approach with a clear success definition.
- Serves as the enterprise expert. Acts as the AI and analytics expert on cross-functional teams for large strategic initiatives, contributes to the growth of the analytic community, and represents the domain to senior leadership.
- Participates in special projects and performs other duties as assigned.
Required Qualifications
- Experience: Minimum ten years of related work experience in analytical roles, including demonstrated people leadership.
- Generative and agentic AI: Hands-on experience designing, evaluating and productionizing LLM-based systems — retrieval-augmented generation, tool and function calling, multi-agent orchestration, and guardrail implementation.
- Evaluation: Proven experience building quantitative evaluation frameworks for non-deterministic systems, including rubric design, benchmark construction and regression testing.
- Data wrangling and engineering: Strong programming skills to access, transform and prepare large-scale structured and unstructured data for modeling. Python required; SQL and modern data platform experience expected.
- Statistical and ML methods: Deep applied command of statistical inference, experimentation and machine learning methods.
- Governance: Working knowledge of model risk management, responsible AI frameworks and controls in a regulated environment.
- Domain: Experience in advice, wealth management, or financial planning — including familiarity with managed advice, portfolio construction, goals-based planning, advisor workflows, client segmentation, or retirement outcomes.
- Education: Undergraduate degree in Analytics, Applied Mathematics, Computer Science, Economics, Statistics or a related analytical field, or an equivalent combination of training and experience.
Preferred
- Graduate degree in a quantitative or computational discipline.
- Cost and capacity management for inference workloads at enterprise scale, including model routing and tiered serving strategies.
- Fine-tuning and adaptation experience — supervised fine-tuning, preference optimization, distillation or domain adaptation of open-weight models.
- Regulatory fluency with fiduciary standards, suitability, supervision and disclosure requirements in wealth and advice.
- Published or presented work in applied AI, or contribution to internal or external AI standards and communities
Special Factors
Sponsorship
Vanguard is not offering visa sponsorship for this position.
About Vanguard
At Vanguard, we don't just have a mission—we're on a mission.
To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.