Data Scientist

Rice University•United States,

3d•$105,000•Remote

About The Position

Rice University’s Departments of Computer Science and Materials Science and NanoEngineering are hiring a Data Scientist to build and operate the data infrastructure for READINESS, a $20 million NSF-funded project transforming four materials-synthesis systems into an AI-driven, remotely accessible autonomous laboratory. The facility will generate diverse data, including synthesis recipes, reactor time-series, user interactions, microscopy images, spectra, and robot telemetry. The Data Scientist will develop and maintain the central data lake that captures, indexes, secures, and serves this information across the project. The role combines data engineering and analysis. It includes deploying an S3-compatible object store, developing ingestion connectors for instruments and robot controllers, establishing SQL and vector-search capabilities under Rice single sign-on, computing embeddings over images and spectra, developing retrieval and similarity-search services, and building AI agents that answer researchers’ questions using project data. Reporting to the faculty leads of the READINESS Data Infrastructure working group, the Data Scientist will collaborate with synthesis platform teams, robotics and AI/digital-twin working groups, Rice IT and Information Security, and graduate and undergraduate students. This is a hands-on position with substantial ownership of central project infrastructure. Materials science, chemistry, or laboratory-science experience is not required; domain knowledge can be developed through collaboration with scientific experts. The successful candidate will help establish reliable, documented systems for capturing, organizing, and providing access to data generated across the facility. This position is fully remote, with work permitted from any location within the United States. Working hours will remain Central Standard Time. Under Rice policy 440, work arrangements may be subject to change. The hiring range is 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 depending on continued funding availability, research needs, and performance.

Requirements

Bachelor’s degree in computer science, data science, engineering, or a related quantitative field.
At least three 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, and documented code.
Proficiency with 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, or Spark.
Working knowledge of Linux administration, containers such as 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, audit logging, and secure research-data handling.
Ability to connect software to physical instruments and devices using network or file-based protocols.
Ability to learn an unfamiliar scientific domain, translate collaborators’ requirements into working systems, manage competing requests, and communicate clearly.

Nice To Haves

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 or greater scale.
Experience with GPU-accelerated similarity search tools such as FAISS, Milvus, or Qdrant, and serving open-weight LLMs.
Experience in academic research or national laboratories, including collaboration with institutional IT and security offices.
Experience monitoring production data systems and implementing backup and recovery.
ROS/rosbag experience.

Responsibilities

Design, deploy, and operate the READINESS central data lake, including its S3-compatible object store, bucket layout, versioning, and access policies.
Develop and maintain ingestion connectors to capture data unattended from synthesis tools, characterization instruments, and robotic systems, landing it with structured, de-identified metadata and provenance links.
Design and maintain the project’s metadata and provenance schema and the Python client library used by project teams to read and write data.
Deploy and administer the SQL query layer, including Trino and Hive Metastore over Parquet, and provide programmatic APIs for researchers, the user portal, and digital-twin and AI teams.
Build embedding pipelines and GPU-resident vector indexes over images, spectra, and logs, and provide similarity-search and retrieval-augmented-generation services.
Develop and demonstrate AI agents that answer questions about experiments by retrieving information from the data lake, in collaboration with the AI & Digital Twins working group.
Implement and maintain security controls, including Rice single sign-on with MFA, tiered role-based access, encryption, and immutable audit logging; collaborate with Rice Information Security and Research Security on design reviews and compliance.
Monitor system health, perform integrity checks and backups, and document and test recovery procedures.
Gather requirements from synthesis platform, characterization, and robotics teams; attend platform-team and working-group meetings; and establish node-wide standards for data formats, metadata, and access.
Write 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.