Extragalactic Data Scientist & AI Tool Developer

California Institute of Technology; Pasadena (CA); On-site; USD 78,000 - 120,000; Full time

Job summary

Caltech IPAC invites applications for a full-time scientist to monitor data quality across ingestion flows and performance of database analyses to ensure internal consistency. You will explore extragalactic archive science enabled by NED and contribute to higher data ingestion rates and reliability.

As a NED Staff Scientist, you will validate automated cross-matching results, develop scalable validation tools, and work with scientists and engineers to accelerate data fusion.

Qualifications

• PhD in astronomy, physics, computer science or related field.
• Two years of post-PhD astronomy experience.
• Knowledge of astrometry, photometry, and galaxy classifications.

Responsibilities

• Apply NED’s methods for locating, extracting, transforming, and integrating data.
• Perform science-based validation and vet automated cross-matching results.
• Develop new tools (e.g., AI-powered) for data quality validation at scale.
• Collaborate with scientists and engineers to improve data fusion into NED.
• Respond to user queries related to the scientific content of NED.
• Design and validate NED services and interfaces for science use cases.
• Conduct archival and data synthesis research.

Skills

Post-PhD experience in astronomy

Coordinate systems knowledge

Data validation experience

Programming experience

Education

PhD in astronomy, physics, computer science or related field

Tools

Python

SQL/Databases

AI/ML tooling

Job description

Caltech IPAC invites applications for a full-time scientist to monitor data quality across ingestion flows and performance of database analyses to ensure internal consistency. You will explore extragalactic archive science enabled by NED and contribute to higher data ingestion rates and reliability.

As a NED Staff Scientist, you will validate automated cross-matching results, develop scalable validation tools, and work with scientists and engineers to accelerate data fusion.