Data Scientist

The Rice University Department of Computer Science and Department of Materials Science and Nano Engineering 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.

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.

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.

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.

• 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.

• 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…