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About This Role
Caltech is a world-renowned science and engineering institute that marshals some of the world's brightest minds and most innovative tools to address fundamental scientific questions. We thrive on finding and cultivating talented people who are passionate about what they do. Join us and be a part of the diverse Caltech community.
Job Summary
NExScI at Caltech/IPAC is seeking a data scientist to join our software development team and drive the continued development of AstroFetch, an AI-powered assistant that helps astronomers query, analyze, and visualize data from astronomical archives. This is a technical software position focused on developing, testing, and maintaining the AI tooling, data interfaces, and analysis tools behind AstroFetch. It is an excellent fit for a developer or data scientist who enjoys writing high-quality code, wants to build applications with large language models, and has enough familiarity with astronomy or another physical science to create tools that researchers can trust.
AstroFetch uses large language models to translate natural-language questions into archive queries, data analysis, and visualizations. A public beta version already exists, built in Python and supporting multiple LLM providers (Anthropic Claude, OpenAI, Google Gemini, and local models). You will develop the software that makes its answers accurate and useful: building the prompts, tools, and retrieval systems it relies on, creating automated evaluations that measure its performance, and extending it to new data sources and capabilities. You will work closely with software and infrastructure engineers, who share responsibility for deploying, securing, and operating the service, and with NExScI scientists, who provide domain expertise and user feedback.
This role will work within a vibrant team of scientists and developers at the NASA Exoplanet Science Institute (NExScI). As a part of IPAC, NExScI (nexsci.caltech.edu) provides archive services, community support, science operations, and analysis tools related to the discovery and characterization of planets beyond our solar system (exoplanets) using data from observatories in space and on the ground. IPAC also hosts several other NASA and Caltech data archives, and this position will have the opportunity to bring AI-driven tools and approaches to those projects as well.
Essential Job Duties
Your primary focus will be the scientific quality and capabilities of AstroFetch, while also exploring new applications of AI across IPAC. Key responsibilities:
- Improve how AstroFetch answers scientific questions through prompt design, tool design, and retrieval-augmented generation (RAG) over archive documentation and data schemas.
- Build evaluation datasets and benchmarks that measure the scientific accuracy of AstroFetch’s queries, analyses, and answers, and use them to guide development and compare LLM providers.
- Develop and validate the analysis and visualization tools the assistant uses with catalogs, time series, spectra, and other archive data products, including the move from static plots to interactive visualizations (e.g., Bokeh or Plotly).
- Connect AstroFetch to additional archives (such as the Keck Observatory Archive, and IRSA), working with the scientists and archive teams who know those data best.
- Write clean, tested, well-documented Python and contribute to a shared codebase through version control, code review, and continuous integration, in partnership with the engineers who lead deployment, security, and infrastructure.
- Gather feedback from astronomers using AstroFetch, analyze how it is used, and turn what you learn into improvements.
- Identify, prototype, and evaluate new applications of AI and large language models across IPAC’s data archives and scientific workflows.
- Stay current with developments in AI for science, share them with colleagues, and present AstroFetch and related work to the astronomy community, including at conferences and in publications where appropriate.
Basic Qualifications
If you have the following in your background, then we want to hear about your interest in joining our team:
- Bachelor’s degree in Computer Science, Data Science, Astronomy, Physics, or a related technical field.
- Strong Python skills, including the scientific Python ecosystem (e.g., NumPy, pandas, Astropy, Matplotlib).
- Experience working with astronomical or other large scientific datasets, including querying data with SQL or a similar language (e.g., ADQL).
- Hands-on experience using large language model APIs or AI tools in code (e.g., Anthropic Claude, OpenAI, Google Gemini); experience from research or personal projects is welcome.
- Strong communication skills and enthusiasm for working with both scientists and software engineers.
Preferred Qualifications
These additional qualifications may give you a strong start, though we still encourage you to apply even if you don’t have all of them:
- Master’s degree in Computer Science, Data Science, Astronomy, Physics, or a related technical field.
- Research experience in exoplanets, time-domain astronomy, or observational astronomy.
- Familiarity with astronomical archives and Virtual Observatory standards (TAP, ADQL) and tools such as astroquery or PyVO.
- Experience building retrieval-augmented generation (RAG) or agentic AI applications with tool use and structured outputs.
- Experience designing evaluations or benchmarks for machine learning or LLM systems.
- Experience with interactive data visualization libraries (Bokeh, Plotly, or similar).
- Experience with Python web frameworks (FastAPI, Flask), Git-based collaboration, automated testing, and CI.
- Familiarity with Docker, PostgreSQL, and cloud services.
- A record of open-source scientific software, public code, or publications.
Required Documents
- Full resume or CV
- Cover letter
- Optional: Provide links to code (e.g., GitHub) or relevant publications
Application Details
- Hybrid position: Typically, three to four days per week on the Caltech campus in Pasadena, California, and one to two days remote, depending on operational needs. Depending on start date, an initial remote working arrangement may be considered.
- Applications will be reviewed on a rolling basis until the position is filled.
Life at IPAC and Caltech
Caltech is an award-winning workplace and one of the best places to work in the greater Los Angeles area. We offer an exciting and dynamic work environment, career development opportunities, competitive pay programs, great benefits, and the opportunity to participate in many campus programs and activities.
People choose to work at IPAC for many reasons, and the casual, employee-centric culture often leads to fulfilling, long-term careers and lasting relationships. We seek the best and brightest talent to join our workforce and become members of our community.
Hiring Range
$41.25 - $47.00 Per Hour
The salary of the finalist(s) selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training.
As one of the largest employers in Pasadena, CA, Caltech is committed to providing comprehensive benefits to eligible employees and their eligible dependents. Our benefits package includes competitive compensation, health, dental, and vision insurance, retirement savings plans, generous paid time off (vacation, holidays, sick time, parental leave, bereavement, etc.), tuition reimbursement, and more. Non-benefit eligible employees will have access to some benefits such as onsite counseling and sick time. Learn more about our benefits and staff perks.
EEO Statement
Caltech is an equal opportunity employer and, as a federal contractor, takes affirmative action to employ qualified individuals with disabilities and protected veterans. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic protected by law.
Disability Accommodations
Caltech complies with the Fair Employment and Housing Act (FEHA) and the Americans with Disabilities Act (ADA). We consider reasonable accommodation measures that may be necessary for eligible applicants and employees to perform the essential functions of a position.
If you would like to request an accommodation to complete this application, interview, or otherwise participate in the employee selection process, please contact Caltech Recruiting at [email protected].
Additionally, if you do not meet the basic qualifications of a role but believe you can perform the essential functions of the job with reasonable accommodation, please reach out to Caltech Recruiting at [email protected].
Salary Context
This $85K-$97K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At California Institute of Technology, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($91K) sits 52% below the category median. Disclosed range: $85K to $97K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
California Institute of Technology AI Hiring
California Institute of Technology has 2 open AI roles right now. They're hiring across Data Scientist, Research Engineer. Based in Pasadena, CA, US. Compensation range: $97K - $97K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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