NFR Semantic Solutions, Associate , LCD Data and Analytics

We're seeking someone to join our team as a Associate in Legal and Compliance Division. This team supports critical NFR activities, such as risk identification, assessment, and management, by developing capabilities based on a blend of symbolic and statistical AI

In the Legal & Compliance division, we assist the Firm in achieving its business objectives by facilitating and overseeing the Firm's management of legal, regulatory and franchise risk. This is an Associate level position within the NFR Data & Analytics is a team in LCDCoE to contribute to a number of innovative projects utilizing semantics, natural language processing, machine learning, and other cutting edge technological approaches. This is an opportunity to learn about and contribute to the next generation of risk technology. The selected candidate would fill a key role and have the opportunity to work closely with global business process owners, technology service providers, and key stakeholders across the Firm

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you'll do in the role:

• Architect and implement machine assisted semantic capabilities that leverage Natural Language Processing (NLP), Data Science, Knowledge Graph reasoning and AI/ML based techniques to execute the NFR Data Strategy.
• Develop and enhance core semantic functionalities (semantic search, recommendations, classification, auto-categorization, similarity detection, and semantic enrichment) powered by NLP, ML, and/or LLM based techniques
• Partner extensively with product owners, framework teams and SMEs to translate business requirements into clear modeling needs aligned with underlying semantic assets, including ontologies, taxonomies, and other graph models.
• Work with platforms such as Metaphactory, helping design SPARQL-powered cached queries and views, knowledge panels, and user-facing semantic explorations.
• Collaborate with Technology teams to deploy and orchestrate solutions that are graph-compatible
• Maintain clear technical documentation of modeling decisions, data needs, core logics, evaluation methodologies, and audit ready artifacts.
• Ensure adherence to model governance, documentation, monitoring, and other compliance requirements throughout the solution lifecycle.
• Contribute to internal best-practices on semantic modeling, NLP/AI advancements, dataset onboarding and quality assurance

• At least 2 years of professional experience in financial services or similarly regulated industries, with exposure to risk, compliance, operations, or other domains requiring strong control and governance sensitivity.
• Strong technical background in data science, NLP, machine learning, ontologies, or semantic architecture-with some hands-on experience in ontology modeling, knowledge graphs.
• Solid programming skills in Python (or similar languages) with the ability to build automation, craft SPARQL queries, develop and deploy NLP models, aligned to semantic and ontology structures.
• Foundational understanding of Non Financial Risk (NFR) concepts or experience in adjacent disciplines such as Operations, Finance, Compliance, or Enterprise Risk Management is also good to have.
• Experience delivering solutions in Agile environments, including refining requirements, managing iterative development cycles, conducting user acceptance testing, gathering stakeholder feedback, and supporting production rollout.
• Exposure to data management, data governance, or information management practices, including an understanding of data quality, metadata, lineage, and controls.
• Desirable to have: familiarity with semantic platforms (e.g., Metaphactory), prompt engineering, AI/ML based solutions, GraphDB, or data engineering concepts.
• Additional Requirements & Skills
• Bachelor's degree or equivalent experience.
• Excellent interpersonal skills, including the ability to work as part of a team and delegate tasks.
• Must be a self-starter and highly organized; attention to detail is a must.
• Excellent analytical skills required along with strong written and verbal communication skills.
• Should be prepared to become proficient with several semantic applications and standards, including semantic platforms (e.g., Metaphactory, Stardog, GraphDB), SPARQL, RDF, and OWL. Existing proficiency is not required but must demonstrate the capability to learn them.