Job description
Join the Celestial Analytics Institute in Pasadena, a hub for cutting-edge astronomy research and data science. We seek a talented Astronomy Data Scientist to bridge astrophysics and software engineering, transforming terabytes of telescope data into actionable insights. You will collaborate with researchers across time-domain astronomy, imaging surveys, and spectroscopy to advance our understanding of the universe.
We value curiosity, collaboration, and a passion for turning complex data into elegant science. This role offers opportunities for leadership in data-driven projects, publications, and high-impact discoveries.
Responsibility
- Design, implement, and optimize end-to-end data pipelines for large astronomical datasets (imaging, spectroscopy, time-domain).
- Develop scalable software tools, visualizations, and dashboards to enable rapid scientific discovery.
- Collaborate with astronomers to process raw observations, calibrate data, and extract scientifically meaningful metrics.
- Apply statistics and machine learning to classify sources, detect transient events, and model astrophysical phenomena.
- Participate in the planning of upcoming observing campaigns and data analysis strategies.
- Document code, maintain version control, and ensure reproducibility of results.
- Mentor junior researchers and contribute to publications, conference talks, and grant proposals.
- Contribute to outreach and citizen science initiatives to broaden public engagement with astronomy.
Qualification
- Master’s or PhD in Astronomy, Astrophysics, Computer Science, or a closely related field.
- Strong proficiency in Python (NumPy, SciPy, Astropy), SQL, and data visualization tools.
- Experience with large-scale data processing frameworks (e.g., Spark, Dask) and Unix/Linux environments.
- Familiarity with astronomical data formats (FITS, HDF5) and data calibration pipelines.
- Background in machine learning and statistical methods applied to astrophysical problems.
- Excellent collaboration, communication, and scientific writing skills.
- Ability to manage multiple tasks, self-motivate, and work in a dynamic, cross-disciplinary team.
- Commitment to reproducible research practices and open science.