Data Modeler Associate

Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.

As part of Risk Management and Compliance, you are at the center of keeping the firm strong and resilient. You help JPMorgan

Chase grow its business in a responsible way by anticipating new and emerging risks and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

As the Data Analyst & Modeler in the Office of Regulation and Document Governance (ORDG), you will be the hands‑on practitioner who takes ORDG’s policy, standard, procedure and regulatory content apart at the atomic level — clauses, obligations, controls, defined terms, cross‑references and their relationships — and turns it into structured, analyzable, machine‑readable data. Reporting to ORDG’s Data Governance & Data Product Lead and working alongside the Data Modeler & Business Analyst and the Data Analytics Lead, you will use data‑science techniques (statistical analysis, NLP, entity and relationship extraction, clustering, similarity and graph methods) to profile the estate, prototype logical and physical models, validate them against real content, and generate the evidence base that governance and product decisions are made on.

You will be equally comfortable in a notebook and in a modelling tool, and will pair with Data Science, Technology and consuming platforms (FPSP, DOGMA, LRCM and the RM&C Bulletin Tool) to make sure what we design at the whiteboard actually holds up on the real corpus.

• Decompose the document corpus to an "atomic" level (clauses, obligations, controls, defined terms, and cross‑references) and convert it into structured data
• Apply text analytics methods (e.g., entity and relationship extraction, similarity, clustering, and topic modelling) to map how documents connect and inform data‑product structures
• Profile the governed data estate for completeness, duplication, drift, orphaned references, and structural anomalies, and report findings that inform design and remediation priorities
• Support knowledge‑graph representations that make cross‑document relationships queryable for retrieval, impact analysis, and AI grounding
• Prototype and iterate conceptual, logical, and physical data models (entities, attributes, cardinality, hierarchy, and lineage) and validate them against real content at scale
• Build and maintain reference data for canonical taxonomies, including mappings from source content to canonical forms
• Partner with Technology on implementation details (data contracts, schema binding, transformation logic, and validation rules) and support testing for new or amended data products
• Design and run analyses and experiments using Python and SQL to answer data‑product and governance questions (e.g., overlap analysis, citation graphs, and term drift) and explain results clearly to non‑specialists
• Support evaluation of retrieval and extraction quality, including labelling, gold‑set curation, and error analysis for AI‑generated structured output
• Produce analytical artefacts for governance decisions (e.g., profile reports, coverage analysis, similarity mapping, and change impact assessments) with a clear audit trail from source content to conclusion
• Help refine the data‑products backlog by breaking down requests into work items, estimating effort, and sequencing delivery with partners

• Experience in a hands‑on data…