Data Scientist, Senior Specialist

The Vanguard Group•Charlotte, PA

6d•Hybrid

About The Position

Serve as a senior technical leader for traditional and agentic AI/ML capabilities supporting enterprise conversational AI across voice, chat, and emerging agentic experiences. Provide technical direction and guide complex solutions from design through production. Drive Data Science across the 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- and retrieval-based solutions. Advance evaluation and production intelligence for conversational AI by establishing rigorous methods to assess functional accuracy, user-need fulfillment, reliability, and other system-level behaviors. Develop scalable evaluation approaches that help teams identify failure modes, compare solutions, establish benchmarks, and make evidence-based release decisions. Use analytical and statistical methods to examine complex datasets, diagnose model and system behavior, perform root-cause analysis, design experiments, quantify uncertainty, and turn findings into product and engineering recommendations. Partner with Product, Engineering, Architecture, UX, Analytics, Risk, and other stakeholders to translate ambiguous business and client problems into measurable Data Science problems and scalable AI solutions. Establish technical standards for model development, experimentation, evaluation, validation, observability, explainability, documentation, and responsible AI use. Communicate findings and recommendations to technical and non-technical audiences, influence cross-functional decisions, and stay current with advances in AI and Data Science. Vanguard uses a hybrid working model for the majority of its crew members, supporting flexibility alongside in-person learning, collaboration, and connection. Vanguard is not offering visa sponsorship for this position.

Requirements

At least 10 years of relevant Data Science, Machine Learning, or applied AI experience, with demonstrated experience leading complex technical work.
Strong practical foundation in machine learning, including supervised and unsupervised learning, feature engineering, model development, validation, experimentation, and performance measurement.
Expertise in Natural Language Processing and Generative AI, including 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 orchestration or routing, tool use, knowledge retrieval, multi-turn conversational behavior, and agent evaluation.
Strong Python and data-analysis skills, experience with large datasets, and familiarity with modern ML/Data Science libraries and development environments.
Experience with AI/ML evaluation and observability, including evaluation datasets, metrics, benchmarks, automated evaluations, production monitoring, failure analysis, and linking system behavior to client or business outcomes.
Ability to translate loosely defined problems into hypotheses, analytical approaches, measurable success criteria, and actionable recommendations while balancing technical depth with business context.
Ability to lead and develop Data Scientists, provide thoughtful technical review, establish engineering and modeling standards, manage competing priorities, and foster accountability, learning, and experimentation.
Strong data analytics, visualization, and storytelling skills.
Strong communication and stakeholder-management skills, including the ability to explain complex AI concepts and influence Product, Engineering, Architecture, Analytics, Risk, and senior business stakeholders.
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.

Nice To Haves

Prior people leadership or technical team leadership experience is strongly preferred.
An advanced degree is preferred.

Responsibilities

Provide senior technical leadership and direction for traditional and agentic AI/ML capabilities supporting enterprise conversational AI across voice, chat, and emerging agentic experiences.
Guide complex AI/ML solutions from design through production.
Lead Data Science work across the AI lifecycle, including problem formulation, research, experimentation, solution design, development, evaluation, deployment readiness, production measurement, and continuous optimization.
Lead development and optimization of supervised and unsupervised learning, NLP, Generative AI, LLM-based applications, agentic systems, classification models, and knowledge- and retrieval-based solutions.
Establish rigorous methods to evaluate conversational AI functional accuracy, user-need fulfillment, reliability, and other system-level behaviors.
Develop scalable evaluation approaches to identify failure modes, compare solutions, establish benchmarks, and support evidence-based release decisions.
Analyze complex datasets to identify patterns, diagnose model and system behavior, perform root-cause analysis, design experiments, quantify uncertainty, and provide actionable recommendations.
Turn complex data into clear insights using exploratory analysis, visualization, statistical reasoning, and business context to inform diagnostic metrics and product improvements.
Partner with Product, Engineering, Architecture, UX, Analytics, Risk, and other stakeholders to translate 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 AI use.
Use evaluation and production signals to develop hypotheses, run experiments, improve models, prompts, and systems, and measure outcomes.
Communicate AI/ML concepts, findings, risks, and recommendations to technical and non-technical stakeholders and influence cross-functional decisions.
Stay current with AI and Data Science advances and evaluate emerging methods, models, frameworks, and architectures.
Lead and develop Data Scientists, provide technical review, establish standards, manage competing priorities, and foster accountability, learning, and experimentation.