Architecture is data—and at JPMorgan Chase, we are transforming how that data drives strategy. This role offers a unique opportunity to build a first-of-its-kind architecture intelligence capability at enterprise scale. You will shape how leadership understands complexity, risk, and evolution across one of the world’s most sophisticated technology estates. Working alongside senior architects and C-suite stakeholders, your insights will directly influence technology direction. If you are driven by data, curious by nature, and motivated to create impact, this role is designed for you.
As a Data Scientist Lead in Asset Management Technology (Architecture & Engineering), you build and scale a data-driven architecture intelligence capability that transforms complex technology data into actionable insights. You partner with senior architects and technology leaders to drive informed decision-making using structured and unstructured data sources, you enable visibility, improve data quality, and apply advanced analytics and AI techniques to uncover trends, risks, and opportunities. You contribute to a culture of evidence-based architecture while shaping the future of enterprise technology strategy.
**Job Responsibilities **
- Own the data quality posture of the Architecture Workbench, identifying gaps and improving completeness across architecture datasets
- Design automated data quality frameworks, including validation checks, scoring mechanisms, and exception reporting pipelines
- Collaborate with domain architects to remediate data issues and embed sustainable data governance practices
- Develop executive-grade dashboards and observability tools to monitor architecture data health and coverage
- Analyze architecture data to uncover trends, risks, and optimization opportunities within the technology estate
- Create recurring analytical products, including reports and visualizations, that address strategic and operational questions
- Track temporal changes in architecture to evaluate alignment with strategic direction and identify emerging deviations
- Translate complex analytical findings into clear narratives for senior technology and business stakeholders
- Identify critical unanswered architecture questions and build datasets and pipelines to address them systematically
- Apply AI and LLM techniques to automate discovery, classification, summarization, and insight generation
- Integrate disparate data sources into unified, queryable models enabling scalable and repeatable intelligence generation
Required qualifications, skills, and capabilities
- Strong hands-on experience with graph databases, particularly Neo4j — including Cypher query authorship, schema design, and graph analytics
- Expert proficiency in SQL and relational data modelling, with experience querying complex, multi-schema environments
- Solid Python skills for data engineering, analysis, and pipeline development (pandas, SQLAlchemy, networkx, or equivalent)
- Demonstrable experience applying LLM and AI techniques to analytical or data problems — RAG pipelines, embedding-based search, prompt engineering, or similar
- Experience building data quality frameworks including automated validation, completeness scoring, and exception management
- Strong data visualisation skills — able to produce executive-grade analytical outputs using tools such as Plotly, Grafana, or custom-built dashboard.
- Experience working with firmwide enterprise data platforms, API-sourced data, and semi-structured or unstructured sources