Lead Geospatial Data Scientist – CB Distribution Analytics
| Company | JP Morgan Chase |
|---|---|
| Location | Columbus, OH, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Mid Level, Senior |
Requirements
- Masters degree in an analytical field (e.g. Data Science, Statistics, Economics, Applied Math, Operations Research, Physics, or GIS fields) and 4 years of related experience. Doctoral degree in lieu of 4 years of related experience.
- Excellent communicator who is able to convey information in an understandable, compelling, and persuasive manner to business partners and senior executives.
- Expert in developing and implementing data science models (e.g. k-means clustering, random forest regression, XGBoost, etc.)
- Strong understanding of spatial data science and GIS applications (e.g., Esri, QGIS, PostGIS, Carto, Apache Sedona).
- Strong understanding of best practices in spatial data storage and management and up-to-date on recent advancements in this space (e.g. geoparquet).
- Sophisticated understanding of foundational models, LLMs, and generative AI as well as recent advancements in multi-modal geospatial foundational models.
Responsibilities
- Work with partners in Real Estate, Corporate Strategy, Distribution Strategy, Data and Analytics, and various lines of business to understand and document analytic project objectives, deliverables, and timelines. Ensure constant two-way communication with partners throughout the analytic journey.
- Execute on analytics roadmaps to support Chase’s branch network optimization initiatives and the impact of these decisions.
- Robustly document developed analytics adhering Chase’s internal and external control obligations.
- Partner with Geospatial Area Product to influence product roadmap to support implementation of geospatial foundational models aligning with Chase’s long term strategic goals.
- Partner with data engineers across the business to ensure support for multimodal geospatial data and its compatibility with analytic initiatives.
- Manage a team of 2-3 data scientists which includes managing the team’s backlog, setting performance and development goals, and other managerial responsibilities.
Preferred Qualifications
- Domain specific expertise in financial services, retail distribution strategy, fraud/risk analytics, and bank operations preferred, but not required.