Job description
At AstralSight Institute, we are advancing the frontiers of observational astronomy through data driven discovery. We are seeking an experienced Astronomy Data Scientist to design and implement end to end data pipelines for large sky surveys, develop processing tools, and collaborate with astronomers to extract scientific insights. This role blends software engineering, data science, and scientific analysis in a fast paced research environment.
As a member of our science data team, you will work on scalable pipelines, calibration routines, and reproducible workflows that help transform raw telescope data into discoveries about the universe.
Responsibility
- Design, implement, and maintain end to end data pipelines for large astronomical surveys using Python, SQL, and cloud services.
- Develop data quality checks, calibration routines, and reproducible analysis workflows.
- Collaborate with astronomers to translate scientific questions into analysable metrics and experiments.
- Apply advanced statistical methods and machine learning to detect transient events and anomalies.
- Document software, pipelines, and results; provide training and mentorship to junior team members.
- Coordinate with telescope operators and instrument teams to ensure data integrity and timely delivery.
- Prepare and present results for internal reviews and external publications; contribute to grant proposals.
- Share best practices in code, data governance, and reproducible research across the institute.
Qualification
- PhD in Astronomy, Astrophysics, or a closely related field, or equivalent industry experience.
- Strong programming skills in Python; experience with C/C++, Jupyter, and scientific libraries such as NumPy, SciPy, and Astropy.
- Experience with data mining, visualization, SQL databases, and large-scale data processing (e.g., Spark, cloud platforms).
- Hands on experience with telescope data, calibration, and image processing pipelines is a plus.
- Familiarity with version control (Git), software testing, and reproducible research practices.
- Strong communication skills; ability to work with multidisciplinary teams and present complex results clearly.
- Proven track record of scientific contributions (publications, conference talks).
- Ability to manage multiple projects and adapt in a fast paced research environment.