Senior Data Scientist: LLMs & Infra Analytics

Oracle; Nashville (TN); On-site; USD 101,000 - 210,000; Full time

Job summary

Oracle US seeks a Senior Data Scientist to join the Cloud Infrastructure Data Science and Analytics team. You will lead data-driven research, build scalable models, and deploy LLM-based solutions to optimize infrastructure and data-center materials use cases.

You will collaborate with engineers, product teams, and stakeholders to define data requirements, design experiments, and deliver actionable insights that support regional expansions and operational efficiency.

Qualifications

• Bachelor’s degree or equivalent practical experience in a quantitative field.
• Strong foundation in probability, statistical inference, experimental design, hypothesis testing, regression, and predictive modeling.
• Experience applying rigorous statistical analysis to large datasets to answer business questions, quantify uncertainty, and communicate actionable insights.
• Proficiency in SQL and Python or R for data extraction, transformation, analysis, visualization, and reproducible modeling workflows.
• Experience with data warehousing and working with structured and semi-structured data at scale.
• Demonstrated ability to communicate analytical findings clearly to both technical and non-technical stakeholders.
• Hands-on experience developing, validating, and deploying machine-learning models, including model selection, feature engineering, performance evaluation, and monitoring.
• Experience evaluating and adapting LLM or generative-AI solutions for business use cases, including prompt design, benchmarking, and quality assessment.
• Collaborative problem-solving skills and the ability to develop, evaluate, and iterate on analytical solutions in an ambiguous environment.

Responsibilities

• Lead exploratory research and rigorous statistical analysis to translate complex data into clear, actionable answers to strategic business questions; apply data transformation, experimental design, predictive modeling, and machine‑learning methods as appropriate.
• Design, fine‑tune, and optimize scalable algorithms and models, ensuring strong reliability, performance, reproducibility, and operational readiness in high‑volume production environments.
• Partner with data scientists, engineers, product teams, and business stakeholders to define data requirements, evaluate analytical opportunities, and deliver solutions aligned with measurable business outcomes.
• Establish and champion data‑quality standards, including validation, monitoring, lineage, and remediation practices, recognizing that reliable inputs are foundational to trustworthy analytical and ML outputs.
• Mentor junior data scientists on statistical rigor, modeling best practices, experiment evaluation, code quality, and effective communication of data‑driven findings.
• Lead the development, evaluation, and fine‑tuning of LLM‑based solutions for infrastructure‑build and data‑center‑materials use cases, including domain adaptation, retrieval/evaluation strategies, and performance measurement.
• Stay current on advances in statistics, machine learning, and data science; assess and apply emerging methods to improve business processes, product capabilities, and decision quality.
• Stay up-to-date with the latest developments in machine learning, statistics, and data science, applying new techniques for process/ product improvements.

Skills

SQL

Python

R

Statistical analysis

Data warehousing

Communication

ML deployment

LLM evaluation

Collaboration

Education

Bachelor’s degree or equivalent in a quantitative field

Job description

Oracle US seeks a Senior Data Scientist to join the Cloud Infrastructure Data Science and Analytics team. You will lead data-driven research, build scalable models, and deploy LLM-based solutions to optimize infrastructure and data-center materials use cases.

You will collaborate with engineers, product teams, and stakeholders to define data requirements, design experiments, and deliver actionable insights that support regional expansions and operational efficiency.