The Rice University Department of Computer Science and Department of Materials
Science and NanoEngineering 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.
Special Instructions to Applicants:
Applicants must submit a CV, contact information for three professional
references, and responses to three required supplemental application questions.
These questions are specific to the position and must be completed as part of
the application.
Applications that do not include the required responses may be considered
incomplete.
Ideal Candidate Statement:
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.
Workplace Requirements:
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.
Hiring Range: 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.
Minimum Requirements:
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.
Skills:
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.
Preferences:
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.
Essential Functions:
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
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