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 JPMorganChase 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.
Job responsibilities
- 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
Required qualifications, capabilities and skills
- Experience in a hands-on data analyst, data scientist, or data modeler role, including entity/relationship modelling, data profiling, and analysis on real business content
- Working knowledge of Python and SQL for data analysis, including pandas and notebook-based workflows, and one or more of: scikit-learn, spaCy (or another NLP library), or graph libraries
- Experience applying data-science techniques (statistical analysis, similarity, clustering, entity/relationship extraction, topic modelling) to unstructured or semi-structured text content
- Familiarity with conceptual, logical, and physical data modelling, including normalization, canonical modelling, cardinality, and lineage
- Ability to partner across Technology, Data Science, Product, and business subject matter experts across regions, balancing rigor on definitions with pragmatic delivery
- Familiarity with generative AI tools used for analysis and development, and understanding how governed structured data underpins safe AI consumption
Preferred qualifications, capabilities and skills (max 7 bullets)
- Experience analysing or modelling policy, regulatory, or control-related content (e.g., obligations, controls, standards, procedures)
- Working knowledge of knowledge graphs and graph analytics, including ontology/taxonomy design for entity relationships, citation networks, and controlled vocabularies
- Familiarity with modern data catalog, lineage, and data modelling tools
- Exposure to extraction and evaluation techniques for structured output, including gold-set curation and error analysis
- Familiarity with JPMorganChase data standards and firmwide governance operating models
- Experience with common delivery and reporting tools (e.g., Jira, Confluence, and dashboard tools)
- Experience operating in a controlled environment with governance processes, audit interactions, and documentation rigor
