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About This Role
The Rice University Department of Computer Science and Department of Materials Science and NanoEngineering are seeking a Data Scientist to build and operate the data infrastructure for READINESS, a new $20 million NSF-funded project transforming four materials synthesis systems (CVD, thermal, PVD, and plasma CVD reactors for 2D materials, oxides, and diamond films) into an AI-driven, remotely accessible autonomous laboratory.
The READINESS facility will generate substantial and diverse data, including growth recipes, reactor time-series, user interactions, optical, SEM/TEM and AFM images, spectra, and continuous robot telemetry. The Data Scientist will be responsible for developing and maintaining the central data lake that captures, indexes, secures, and serves this information across the project.
The position combines data engineering and analysis, including deploying an S3-compatible object store, developing ingestion connectors for live instruments and robot controllers, and establishing SQL and vector-search capabilities under Rice single sign-on. The role will also compute embeddings over images and spectra, develop retrieval and similarity-search services, and build AI agents capable of answering researchers' questions directly from the data.
Reporting to the faculty leads of the READINESS Data Infrastructure working group, the Data Scientist will collaborate closely with the synthesis platform teams, robotics and AI/digital-twin working groups, Rice IT and Information Security, and graduate and undergraduate students involved in the project.
This is a hands-on computer science and data engineering position that requires substantial ownership of the project's central data infrastructure. A background in materials science, chemistry, or laboratory science is not required ; domain-specific knowledge can be developed through collaboration with the project's scientific experts. Working within a small team, the Data Scientist will develop the foundational systems through which data generated across the READINESS facility is captured, organized, and made accessible.
Special Instructions to Applicants:
Submit the following with your Rice application:
(1) CV
(2) a short statement explaining how your experience aligns with the position requirements; and
(3) three references including contact information.
Ideal Candidate Statement:
The ideal candidate is a skilled software or data engineer who enjoys collaborating across disciplines and brings experience designing data models and developing reliable systems to capture, organize, and serve complex data. Equally comfortable deploying storage infrastructure, troubleshooting connections to physical instruments, training embedding models, and integrating LLM agents with databases, this individual has experience developing and deploying data systems that others rely on. An appreciation for the challenges and opportunities presented by diverse, real-world data generated by scientific equipment is essential, along with careful attention to system reliability, documentation, and data integrity.
Self-directed and pragmatic, they can translate a project goal into a working technical solution, selecting appropriate tools and collaborating with the researchers who operate the instruments to ensure data is captured accurately and consistently with the necessary metadata. They communicate effectively with colleagues from different technical backgrounds and understand the importance of security, provenance, and access control in a multi-institutional research environment.
Workplace Requirements:
This position is fully remote, permitting all tasks to be completed from any location within the United States. Working hours will remain central standard time. Per Rice policy 440, work arrangements may be subject to change.
Hiring Range: Up to $105,000 annually.
This is a one-year term limited, benefits-eligible position funded by a grant, soft and/or restricted funds, and may be renewed based on continued funding availability, research needs, and performance.
Minimum Requirements:
- Bachelor’s degree in computer science, data science, engineering, or a related quantitative field.
- Three or more (3+) years of related professional experience in data engineering, data science, software engineering, or research computing.
Skills:
- Strong programming skills and ability to write production-quality, tested, documented code.
- Proficiency with SQL and relational data modeling, including scientific or operational database schemas.
- Experience with object storage (S3, Ceph, or MinIO), columnar formats (Parquet), and distributed query engines (Trino, Presto, Spark, or similar).
- Working knowledge of Linux administration, containers (Docker/Kubernetes), and at least one major cloud platform (AWS or Azure).
- Familiarity with PyTorch, embedding models, vector databases, and retrieval-augmented or agentic LLM applications.
- Understanding of authentication and access controls (SSO/SAML/OIDC, role-based access, audit logging) and secure research-data handling.
- Ability to connect software to physical instruments and devices using network or file-based protocols; ROS/rosbag experience is a plus.
- Ability to learn an unfamiliar scientific domain, translate collaborators’ requirements into working systems, manage competing requests, and communicate clearly.
Preferences:
- Five or more years of professional experience building and operating data systems.
- Experience building data pipelines for scientific instruments, laboratory automation, manufacturing, or IoT/telemetry.
- Experience operating Ceph or comparable software-defined storage, or managing cloud object-storage tenancies at 100 TB+ scale.
- Experience with GPU-accelerated similarity search (FAISS, Milvus, Qdrant) and serving open-weight LLMs.
- Experience in academic research or national laboratories, including working with institutional IT and security offices.
- Experience monitoring production data systems and implementing backup and recovery.
Essential Functions:
- Designs, deploys, and operates the READINESS central data lake, including the S3-compatible object store (self-hosted Ceph or cloud), bucket layout, versioning, and access policies.
- Develops and maintains ingestion connectors that capture data unattended from synthesis tools, characterization instruments, and robotic systems, and land it in the store with structured, de-identified metadata and provenance links.
- Designs and maintains the project's metadata and provenance schema and the Python client library that all project teams use to read and write data.
- Deploys and administers the SQL query layer (Trino and Hive Metastore over Parquet) and programmatic APIs for researchers, the user portal, and digital-twin and AI teams.
- Builds embedding pipelines and GPU-resident vector indexes over images, spectra, and logs, and exposes similarity-search and retrieval-augmented-generation services.
- Develops and demonstrates AI agents that answer questions about experiments by retrieving from the data lake, in collaboration with the AI & Digital Twins working group.
- Implements and maintains security controls, including Rice single sign-on with MFA, tiered role-based access, encryption, and immutable audit logging, and works with Rice Information Security and Research Security on design reviews and compliance.
- Monitors system health, performs integrity checks and backups, and documents and tests recovery procedures.
- Gathers requirements from synthesis platform, characterization, and robotics teams; attends platform team and working-group meetings; and sets node-wide standards for data formats, metadata, and access.
- Writes documentation, user guides, and onboarding materials, and trains researchers and partner-site collaborators to use the data infrastructure.
- Mentors graduate and undergraduate students working on data infrastructure projects.
- Performs all other related duties as assigned
Rice University HR | Benefits: https://knowledgecafe.rice.edu/benefits
Rice Mission and Values : Mission and Values | Rice University
Rice University is committed to ensuring Equal Employment Opportunity and welcoming the fullness of diversity into our candidate pools. Rice considers qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national or ethnic origin, genetic information, disability, or protected veteran status. Rice also provides reasonable accommodations to qualified persons with disabilities. If an applicant requires a reasonable accommodation for any part of the application or hiring process, please get in touch with Rice University’s Human Resources Office via email at [email protected] for support.
If you have any additional questions, please email us at [email protected] . Thank you for your interest in employment with Rice University.
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Rice University, 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 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Rice University AI Hiring
Rice University has 1 open AI role right now. They're hiring across Data Scientist. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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