Data Scientist

About the company

Rice University

Higher education · 2 to 10 employees

What you'll do

• 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 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

Key skills

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