Conversational and Generative Data AI Specialist

Morgan Stanley•Town/Village of Harrison, NY

1d•Onsite

About The Position

Morgan Stanley is seeking a highly skilled and motivated Conversational and Generative Data AI Specialist to support the construction, analysis, and refinement of natural language datasets for conversational and generative AI products and solutions. The role requires the ability to apply and communicate AI concepts to a financial services audience, with a strong understanding of natural language processing/understanding, data annotation management, conversation design, and prompt engineering. The specialist must be able to translate complex technical insights into clear, actionable guidance for various stakeholders. Morgan Stanley is a leading global financial services firm with over 1,200 offices in 43 countries, committed to employee growth and meaningful careers. The Wealth Management (WM) Platforms division manages industry-leading platforms to provide a unified digital experience, unlock growth, and deliver efficiencies for Advisors, Clients, and Institutions, and includes ten sub-teams.

Requirements

Proven experience annotating, managing, and evaluating datasets in conversational AI (chatbots, dialogue systems, voice assistants such as Alexa or Google Assistant) and/or generative AI (prompt engineering, generative AI evaluation) contexts.
Bachelor’s degree in linguistics, computational linguistics, data science, or related field.
Familiarity with extracting qualitative and quantitative insights from large natural language corpora.
Ability to function and collaborate within a diverse, cross-functional global organization.
Demonstrated ability to communicate data-driven insights and recommendations to technical and non-technical stakeholders.
Past applicable experience in finance, wealth management, or retail/investment banking.
Basic competency in programming languages commonly used in data analysis, such as SQL and Python.
Hands-on experience building a generative AI product.

Responsibilities

Develop clear, consistent annotation guidelines that ensure high-quality labeled data across multiple diverse use cases.
Manage end-to-end annotation workflows, including task onboarding, annotator support, annotation quality assurance, and performance monitoring.
Analyze annotation output to detect patterns, ambiguities, and failure modes, and adjust guidelines or processes accordingly.
Communicate annotation insights and dataset quality metrics to both technical and non-technical stakeholders to inform modeling decisions and product direction.
Recommend process or guideline adjustments based on recurring insights and performance trends.
Translate qualitative and quantitative feedback into clear recommendations for improving datasets, product behavior, and model performance.