Data Scientist - Remote

About the Company
Rice University’s Department of Computer Science and Department of Materials Science and NanoEngineering are collaborating on READINESS, a new $20 million NSF-funded project focused on developing an AI-driven, remotely accessible autonomous laboratory. The project will connect four materials synthesis systems—CVD, thermal, PVD, and plasma CVD reactors—to support research involving 2D materials, oxides, and diamond films.
The READINESS facility will generate data from laboratory instruments, robotic systems, imaging equipment, spectra, and experimental workflows. The project aims to establish the data infrastructure needed to capture, organize, secure, and make this information accessible to researchers and collaborating teams.
About the Role
Rice University is seeking a Data Scientist to design, build, and operate the central data infrastructure for the READINESS project. This hands-on role combines data engineering, software development, storage infrastructure, and AI-enabled data retrieval. The successful candidate will take ownership of the central data lake, develop connections to laboratory instruments and robotic systems, and establish tools that allow researchers to query, analyze, and retrieve information from complex scientific datasets.
Reporting to the faculty leads of the READINESS Data Infrastructure working group, the Data Scientist will work closely with synthesis platform teams, robotics and AI/digital-twin groups, Rice IT and Information Security, and graduate and undergraduate students. A background in materials science, chemistry, or laboratory science is not required, as domain-specific knowledge can be developed through collaboration with scientific experts.
Key Responsibilities
Design, deploy, and operate the READINESS central data lake, including an S3-compatible object store hosted on Ceph or in the cloud, with appropriate bucket structures, versioning, and access policies.
Develop and maintain ingestion connectors that collect data from synthesis tools, characterization instruments, and robotic systems, including structured, de-identified metadata and provenance links.
Design and maintain metadata and provenance schemas, along with a Python client library for project teams to read and write data.
Deploy and administer the SQL query layer using Trino and Hive Metastore over Parquet, as well as APIs for researchers, the user portal, digital-twin teams, and AI applications.
Build embedding pipelines and GPU-resident vector indexes for images, spectra, and logs, supporting similarity search and retrieval-augmented generation.
Develop and demonstrate AI agents that answer questions about experiments by retrieving relevant information from the data lake in collaboration with the AI and Digital Twins working group.
Implement and maintain security controls, including Rice single sign-on with multi-factor authentication, role-based access, encryption, and immutable audit logging.
Work with Rice Information Security and Research Security on system design reviews and compliance requirements.
Monitor system health, conduct data integrity checks, maintain backups, and document and test recovery procedures.
Gather requirements from synthesis, characterization, and robotics teams, participate in working-group meetings, and establish standards for data formats, metadata, and access.
Prepare technical documentation, user guides, and onboarding materials, and train researchers and partner-site collaborators to use the data infrastructure.
Mentor graduate and undergraduate students working on data infrastructure projects.
Perform other related duties as assigned.
Required Qualifications
Bachelor’s degree in computer science, data science, engineering, or a related quantitative field.
At least three years of professional experience in data engineering, data science, software engineering, research computing, or a related area.
Strong programming skills, including the ability to write production-quality, tested, and documented code.
Proficiency in SQL and relational data modeling, including scientific or operational database schemas.
Experience with object storage such as S3, Ceph, or MinIO; columnar formats such as Parquet; and distributed query engines such as Trino, Presto, Spark, or similar tools.
Working knowledge of Linux administration, Docker or Kubernetes, and at least one major cloud platform, such as AWS or Azure.
Familiarity with PyTorch, embedding models, vector databases, and retrieval-augmented or agentic LLM applications.
Understanding of authentication and access controls, including SSO, SAML, OIDC, role-based access, and audit logging, as well as secure research-data handling.
Ability to connect software with physical instruments and devices through network-based or file-based protocols.
Ability to learn unfamiliar scientific domains, translate collaborator requirements into working systems, manage competing requests, and communicate clearly across technical disciplines.
Preferred Qualifications
Five or more years of professional experience building and operating data systems.
Experience developing data pipelines for scientific instruments, laboratory automation, manufacturing, or IoT and telemetry systems.
Experience operating Ceph or comparable software-defined storage, or managing cloud object-storage environments at a scale of 100 TB or more.
Experience with GPU-accelerated similarity search tools such as FAISS, Milvus, or Qdrant, and serving open-weight large language models.
Experience in academic research or national laboratories, including collaboration with institutional IT and security teams.
Experience monitoring production data systems and implementing backup and recovery procedures.
Experience with ROS or rosbag is a plus.
Salary & Benefits
The hiring range is up to $105,000 annually. This is a one-year, term-limited, benefits-eligible position funded through a grant, soft funds, and/or restricted funds. Renewal may be possible depending on continued funding availability, research needs, and performance.
Location and Work Arrangement
This position is fully remote, with all work performed from within the United States. Working hours will follow Central Standard Time. Under Rice University Policy 440, the work arrangement may be subject to change.
Equal Employment Opportunity
Rice University is committed to equal employment opportunity and welcomes diversity in its candidate pools. Qualified applicants are considered without regard to race, color, religion, age, sex, sexual orientation, gender identity, national or ethnic origin, genetic information, disability, or protected veteran status.
Rice University also provides reasonable accommodations for qualified individuals with disabilities during the application and hiring process. Applicants who need an accommodation may contact the Human Resources Office at facstaffada@rice.edu.
For additional employment questions, contact jobs@rice.edu.

Work arrangement
Yes