VP, Policy Engine & Data Contracts (ODRL/LLM)

Job Description The Chief Data and Analytics Office accelerates the firm’s data and analytics journey by improving security and enabling better decision-making. The Strategy and Execution team defines and delivers vision and strategy, maximizing the value of data globally. This role focuses on shaping the standards that define how data is shared responsibly at the firm and on building the developer-friendly software that puts those models into practice at scale. As a Vice President, Policy Engine and Data Contracts Engineer in the Chief Data and Analytics Office’s Strategy and Execution team, you are both a modeler and a builder. You author the firm’s data-use policy model as a W3C Open Digital Rights Language (ODRL) profile, govern its evolution, and design the data contract representations that rise from it. Simultaneously, you build the Python services, APIs, and tooling that evaluate and enforce those policies — across an RDF triple-store backend and large language model (LLM)-assisted authoring surfaces. You partner with legal, privacy, and engineering stakeholders to turn ambiguous policy intent into precise, machine-readable form, and ship working software in iterative releases. Job Responsibilities Author and maintain the firm’s data-use policy model as a W3C ODRL profile, including the controlled vocabulary, action types, and shapes that constitute it. Design data contract representations on top of the policy model; support authoring, validation, storage, and retrieval of contracts in an RDF triple-store. Build and maintain a Python-based ODRL policy evaluation engine that processes data-use policies and enforces them against runtime requests, including permissions, prohibitions, obligations, and conflict resolution. Develop adapters that translate policies conforming to external ODRL profiles into the internal data-use policy model. Author Shapes Constraint Language (SHACL) shapes that define what a well-formed policy looks like; write validation pipelines, consistency checks, and regression tests for policy graphs. Govern the evolution of the policy vocabulary as new use cases arise; align it with adjacent standards including DCAT, DPROD, and PROV; publish model decisions and changelogs. Build English-to-ODRL and ODRL-to-English translation pipelines, including LLM-assisted drafting and explanation with appropriate guardrails. Deliver APIs and tooling that enable stakeholders to author, review, and understand policies without needing to write RDF directly. Partner with legal, privacy, and engineering teams to clarify ambiguous policy text and formalize it into machine-readable form. Required Qualifications, Capabilities, and Skills Hands-on experience building systems with W3C ODRL, including authoring or extending profiles and evaluating policies against runtime requests. Demonstrated ability to design and evolve information models — controlled vocabularies, ontologies, or data models — ideally in collaboration with non-technical subject-matter experts. Strong Python engineering skills, including building clean, tested, production-ready services. Practical experience with RDF triple-stores (for example, Oxigraph, GraphDB, or Neptune), including SPARQL, graph management, and SHACL validation. Working knowledge of linked-data standards, including RDF, JSON-LD, Turtle, RDFS/OWL, SKOS, and SHACL. Familiarity with adjacent governance and metadata standards, including DCAT, DPROD, PROV, and DQV. Ability to translate ambiguous natural language into precise logical structures, including constraint expressions and rule precedence. Willingness to engage with W3C and open-source linked-data communities to track standards evolution and contribute lessons back upstream. Ability to work in an iterative delivery cadence, including demos, feedback cycles, and incremental releases. Preferred Qualifications, Capabilities, and Skills Direct contributions to W3C working groups, community groups, or open-source linked-data projects. Experience with policy and rule engines (for example, Open Policy Agent and Rego, or custom engines). Familiarity with LLM integration patterns (prompt engineering, structured output, and guardrails) for policy drafting and explanation. Knowledge of data-sharing governance patterns, including purpose limitation, retention, locality, and consent constraints. Experience with attribute-based access control and policy-based access control. Experience building developer-facing tooling (CLIs, APIs, or lightweight UIs) for ontology or policy authoring workflows. Compensation Base salary range: $128,250.00–$205,000.00 for both Jersey City, NJ and New York, NY locations. Eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in cash and/or forfeitable equity. #J-18808-Ljbffr