Data Scientist

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 (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.

• 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.

• Designs, deploys, and operates the READINESS central data lake, including…