Senior Associate Data Modeler

About The Team

• Professionals in Corporate Functions cover diverse areas from finance and risk to human resources and marketing.
• Corporate teams are essential for setting businesses, clients, customers, and employees up for success.

About the Role

• Opportunity to join a dynamic and growing team in a fast-paced and challenging area.
• Partner with the Business to provide a comprehensive view.
• Part of Risk Management and Compliance, responsible for keeping the firm strong and resilient.
• Anticipate new and emerging risks and use expert judgment to solve real-world challenges.
• Culture in Risk Management and Compliance emphasizes 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 a hands-on practitioner.
• Take ORDG's policy, standard, procedure, and regulatory content apart at the atomic level (clauses, obligations, controls, defined terms, cross-references, and their relationships).
• Turn content into structured, analyzable, machine-readable data.
• Report to ORDG's Data Governance & Data Product Lead.
• Work alongside the Data Modeler & Business Analyst and the Data Analytics Lead.
• 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 evidence for governance and product decisions.
• Be comfortable in a notebook and a modeling tool.
• Pair with Data Science, Technology, and consuming platforms (FPSP, DOGMA, LRCM, and the RM&C Bulletin Tool) to ensure designs hold up on the real corpus.

Key Responsibilities

• Decompose ORDG's document corpus at the atomic level (clauses, obligations, controls, defined terms, cross-references, and citations) and build entity and relationship views that describe how policy content connects.
• Profile the governed data estate (completeness, cardinality, duplication, drift, orphaned references, and structural anomalies) and surface findings that inform data-product design, remediation, and prioritization.
• Apply NLP and text-analytics techniques (entity extraction, relationship extraction, similarity, clustering, topic modelling) to reveal structure inside unstructured policy content and translate it into candidate data structures.
• Build and maintain a knowledge-graph view of the corpus (obligations, controls, defined terms, policies, procedures, and their citations and dependencies) to make cross-document relationships explicit and queryable, and to underpin retrieval, impact analysis, and AI grounding.
• Prototype and iterate conceptual, logical, and physical data models for ORDG's governed data products in partnership with the Data Modeller & Business Analyst (including entity/attribute definitions, cardinality, hierarchy, and lineage).
• Validate proposed models against real content at scale (loading representative samples, testing rules, measuring coverage and edge-case behaviour) and feed evidence back into design decisions before physical build.
• Build reference data structures for canonical taxonomies (obligations, controls, defined terms, jurisdictions) and maintain mappings between source content and canonical form.
• Support the Data Modeller & Business Analyst and Technology partners on physical implementation (data contracts, schema binding, transformation logic, validation rules) and hold artifacts accurate as the model evolves.
• Use Python and SQL as everyday tools (pandas, scikit-learn, spaCy / NLP libraries, graph libraries, notebook-based analysis) to interrogate content, prototype models, and generate evidence.
• Design and run experiments to answer specific product and governance questions (e.g., ‘how many obligations overlap across two policies?', ‘what is the citation graph for this domain?', ‘where does a controlled term drift?') and communicate results in a way non-specialists can act on.
• Partner with Data Science on retrieval quality, extraction accuracy, and model evaluation (including labeling, gold-set curation, and error analysis on AI-generated structured output).
• Produce analytical artifacts that inform governance forum decisions (profile reports, coverage analyses, similarity maps, model change impact assessments) with a clear audit trail from raw content to conclusion.
• Contribute to the ORDG analytics estate in partnership with the Data Analytics Lead (including estate-health signals, KPI/KRI feeds, and content-level telemetry) as governed inputs into the unified analytics platform.
• Support user-acceptance testing and data-quality validation for new or amended data products; capture defects, quantify impact, and re-baseline artifacts once accepted.
• Work across time zones and delivery streams from Bangalore; pair daily with governance, modeling, analytics, Technology, and Data Science partners in the U.K., U.S., and India, and hold artifacts as the single source of truth across the handover.
• Contribute to backlog refinement across the Define & Govern Data Products workstream: break down asks into modeling and analytical work items, estimate effort, and sequence delivery with the Data & AI Execution Lead.

Requirements & Tech Stack

• Hands-on practitioner role.
• Working knowledge of Python and SQL for data analysis.
• Familiarity with AI tools, inclusive of LLM Suite, Chat GPT and/or Claude.
• Familiarity with AI-assisted development tools such as GitHub Copilot.
• Experience with common delivery and reporting tooling (e.g., Jira / Confluence and dashboard tools such as Power BI, Tableau or ThoughtSpot).
• Strong Excel skills.

Qualifications

• 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.
• 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) and comfort iterating models against real content.
• Comfortable working across Technology, Data Science, Product Owners, and business SMEs, including U.S., U.K., and India partners; able to hold rigor on definitions while remaining pragmatic on sequencing.
• Understanding of how governed structured data underpins safe AI consumption.

Preferred Qualifications

• Prior experience analyzing or modelling policy, regulatory, or control-related content (including obligations, controls, standards, or procedure text).
• Working knowledge of knowledge graphs and graph analytics (property graphs and/or RDF, Cypher / SPARQL, ontology and taxonomy design) for representing entity relationships, citation networks, and controlled vocabularies.
• Familiarity with modern data-catalog and lineage tooling (e.g., Collibra, Alation, Immuta) and modelling tools (e.g., ER/Studio, Power Designer, or equivalent).
• Exposure to LLM-based extraction and evaluation techniques (including gold-set curation, error analysis, and prompt or fine-tuning approaches for structured output).
• Familiarity with JPMorganChase data standards, CDAO frameworks, and firmwide governance operating models.
• Experience operating in a controlled environment with governance processes, audit interactions, and documentation rigor.