Core Responsibilities
- Serve as a senior technical leader for traditional and agentic AI/ML capabilities supporting enterprise conversational AI across voice, chat, and emerging agentic experiences while providing technical direction and guiding complex solutions from design through production.
- Drive Data Science across the full AI lifecycle, from problem formulation, research and experimentation through solution design, development, evaluation, deployment readiness, production measurement, and continuous optimization.
- Lead development and optimization of traditional and agentic AI/ML solutions, including supervised and unsupervised learning, NLP, Generative AI, LLM-based applications, agentic systems, classification models, and knowledge/retrieval-based solutions.
- Advance evaluation and production intelligence for conversational AI, establishing rigorous methods to assess functional accuracy, user-need fulfillment, reliability, and other system-level behaviors.
- Develop scalable evaluation approaches that enable teams to identify failure modes, compare solutions, establish benchmarks, and make evidence-based release decisions.
- Apply strong analytical and statistical thinking to uncover patterns in large and complex datasets, diagnose model and system behavior, perform root-cause analysis, design experiments, quantify uncertainty, and translate findings into actionable product and engineering recommendations.
- Turn complex data into clear, cohesive stories and actionable insights, combining exploratory analysis, visualization, statistical reasoning, and business context to deliver diagnostic metrics and product improvements.
- Partner closely with Product, Engineering, Architecture, UX, Analytics, Risk and other stakeholders as an embedded technical leader, translating ambiguous business and client problems into measurable Data Science problems and scalable AI solutions
- Establish technical standards and best practices for model development, experimentation, evaluation, validation, observability, explainability, documentation, and responsible use of AI.
- Drive continuous improvement of conversational and agentic systems by converting evaluation and production signals into hypotheses, experiments, model/prompt/system improvements, and measurable outcomes.
- Communicate complex AI/ML concepts, findings, risks, and recommendations clearly to both technical and non-technical stakeholders and influence decisions across cross-functional teams.
- Stay current with advances in AI and Data Science and evaluate emerging methods, models, frameworks, and architectures.
Qualifications
- 10+ years of relevant Data Science, Machine Learning, or applied AI experience, with demonstrated experience leading complex technical work; prior people leadership or technical team leadership strongly preferred.
- Strong practical foundation in machine learning, including supervised and unsupervised learning, feature engineering, model development, validation, experimentation, and performance measurement.
- Demonstrated expertise in Natural Language Processing and Generative AI, with a strong understanding of transformer architectures, embeddings, LLMs, prompting, retrieval-augmented generation, and modern language-model development patterns.
- Hands-on experience designing, building, evaluating, or optimizing agentic AI systems, including areas such as orchestration/routing, tool use, knowledge retrieval, multi-turn conversational behavior, and agent evaluation.
- Strong Python and data-analysis skills, with experience working with large datasets and using modern ML/Data Science libraries and development environments.
- Experience with AI/ML evaluation and observability, including developing evaluation datasets, metrics, benchmarks, automated evaluations, production monitoring, failure analysis, and methods for connecting model/system behavior to client or business outcomes.
- Ability to move comfortably between technical depth and business context, translating loosely defined problems into hypotheses, analytical approaches, measurable success criteria, and actionable recommendations.
- Demonstrated ability to lead and develop Data Scientists, provide thoughtful technical review, establish engineering and modeling standards, manage competing priorities, and create an environment of accountability, learning, and experimentation.
- Strong data analytics, visualization, and storytelling skills to turn complex data into actionable insights.
- Strong communication and stakeholder-management skills, with the ability to explain complex AI concepts and influence Product, Engineering, Architecture, Analytics, Risk, and senior business stakeholders.
Education
- Undergraduate degree in Computer Science, Engineering, Data Science, Statistics, Applied Mathematics, Economics, Analytics, or a related quantitative discipline, or an equivalent combination of education and relevant experience.
- Advanced degree preferred.
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.
