at J.P. Morgan
Bulge Bracket Investment BanksPosted 13 days ago
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**Data Scientist Lead** in Asset Management Technology transforms complex technology data into actionable insights, enabling informed decision-making. Partnerships with senior architects and technology leaders advance data-driven strategy. Key responsibilities involve owning data quality, automating frameworks, collaborating with domain architects, and delivering executive-grade dashboards. Role demands experience with graph databases ( Neo4j ), SQL, Python (pandas, SQLAlchemy), LLM/AI techniques, and data visualization tools. Apply to build and scale architecture intelligence at enterprise scale.
- Compensation
- Not specified
- City
- Bengaluru
- Country
- India
Currency: Not specified
Full Job Description
Location: Bengaluru, Karnataka, India
- 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
