Senior/Principal Data Scientist- Artificial Intelligence- Hybrid

Sandia Corporation•Albuquerque, NM

55d•Hybrid

About The Position

Sandia's artificial intelligence (AI) team is building the U.S. Department of Energy's (DOE) next-generation AI Platform, an integrated scientific AI capability that delivers rapid, high-impact solutions for national security, science, and applied energy missions. The Platform is based on three pillars: Models, Infrastructure, and Data. You will join the Data Pillar team to design, implement, and operate Sandia's AI-ready, zero-trust data ecosystem. Your work will transform raw simulation outputs, sensor and facility logs, experimental records, and production data into governed, provenance-tracked, and access-controlled datasets that power AI models, autonomous agents, and mission workflows across DOE's HPC, cloud, and edge environments. We anticipate multiple hires for the Data Pillar that collectively span the set of responsibilities and skills described below. Likewise, new hires will be expected to work in conjunction with existing Sandia staff and teams from other DOE laboratories to deliver on this ambitious, fast-paced project. Importantly, we anticipate that while AI Platform development will leverage existing AI and data science tools extensively, success will also require considerable innovation and problem solving to address the unique needs of DOE applications. If this sounds like an exciting challenge to you, we look forward to reading your application! You will be part of a multi-disciplinary, mission-focused team delivering foundational data capabilities for transformative AI systems in national security, energy, and critical materials. Occasional travel may be required. If you are passionate about building the data backbone for next-generation AI at scale, we want to hear from you. The selected applicant can work a combination of onsite and offsite work. The selected applicant must live within a reasonable distance for commuting to the assigned work location when necessary.

Requirements

Bachelor's degree in Computer Science, Data Science, Statistics, or a related STEM field, plus five (5) years of directly relevant experience, or an equivalent combination of education and experience
Ability to acquire and maintain a DOE Q clearance

Nice To Haves

Graduate degree (M.S. or Ph.D.) with a significant data research component where an independent research project was a graduation requirement (e.g., independent project, thesis, or dissertation).
Experience in developing software for enterprise and national security applications.
Experience acquiring, preparing, and analyzing real world data
Demonstrated software development skills and familiarity with modern software development practices.
Proven ability to work and communicate effectively in a collaborative and interdisciplinary team environment, guiding technical decisions and mentoring junior staff.
Graduate degree in Data Science, Informatics, Statistics, or a related STEM field with a significant data research component
Background in AI-mediated data curation: automated annotation, feature extraction, and dataset certification
Hands-on knowledge of data security and zero-trust principles, including secure enclaves, attribute-based access control, and data masking or differential privacy
Familiarity with FAIR (Findable, Accessible, Interoperable, Reusable) data practicesCurating and managing scientific or engineering datasets
Data architecture for HPC and edge-computing environments
Advanced data fusion techniques for heterogeneous and streaming data sources
Building data pipelines for feature stores, experiment tracking, and model drift monitoring
Designing and enforcing data policies for classified, export-controlled, or proprietary data
Collaborating on public¿private partnerships or multi-lab federated data efforts
Demonstrated expertise in building and maintaining production data pipelines (ETL/ELT) and data warehouses or data lakes
Proficiency in programming languages such as Python, SQL, and experience with frameworks like Apache Spark or Dask
Familiarity with cloud platforms (AWS, Azure, or GCP) and container orchestration (Kubernetes)

Responsibilities

Build and operate an AI-Ready Lakehouse
Design and maintain a federated data lakehouse with full provenance/versioning, attribute-based access control, license/consent automation, and agent telemetry services
Implement automated, AI-mediated ingestion pipelines for heterogeneous sources (HPC simulation outputs, experimental instruments, robotics, sensor streams, satellite imagery, production logs).
Enforce Data Security & Assurance
Develop a Data Health & Threat program: dataset fingerprinting, watermarking, poisoning/anomaly detection, red-team sampling, and reproducible training manifests
Configure secure enclaves and egress processes for CUI, Restricted Data, and other sensitive corpora with attestation and differential-privacy where required
Define and Implement Data Governance
Establish FAIR-compliant metadata standards, data catalogs, and controlled-vocabulary ontologies
Automate lineage tracking, quality checks, schema validation, and leak controls at record-level granularity
Instrument AI Workflows with Standardized Telemetry
Deploy Agent Trace Schema (ATS) and Agent Run Record (ARR) frameworks to log tool calls, decision graphs, human hand-offs, and environment observations
Treat agent-generated artifacts (plans, memory, configurations) as first-class data objects
Collaborate Across Pillars
Work with Models and Interfaces teams to integrate data services into training, evaluation, and inference pipelines
Partner with Infrastructure engineers to optimize data movement, tiered storage, and high-bandwidth networking (ESnet) between HPC, cloud, and edge
Engage domain scientists and mission leads for agile deterrence, energy grid, and critical minerals use cases to curate problem-specific datasets
Support Continuous Acquisition & Benchmarking
Design edge-to-scale data acquisition systems with robotics and instrument integration
Develop data/AI benchmarks: datasets, tools, and metrics for pipeline performance, model evaluation, and mission KPIs
Author an AI-mediated parser for a new experimental instrument, automatically extracting and cataloging metadata
Implement an attribute-based policy that blocks unapproved data combinations in a classified enclave
Prototype a streaming pipeline that feeds live sensor data from a nuclear facility into an HPC training queue
Develop a dashboard that alerts on data drift, pipeline failures, or anomalous records
Collaborate with MLOps engineers to version datasets alongside model artifacts in CI/CD

Benefits

Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance
Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)

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