Data Scientist 3

 

Preferred Qualifications

• Hands-on experience building or optimizing LLM-backed experiences, including content designed for grounded inference and citation-ready answers.
• Experience with neural networks, neural machine translation, or modern NLP architectures.
• Experience with relevance feedback loops, evaluation harnesses, and model quality monitoring in production settings.
• Background creating enterprise-grade knowledge bases, content hubs, or search/retrieval systems.
• Familiarity with tokenization and encoding methods

Key Responsibilities AI Discoverability & Retrieval Optimization

• Audit and optimize how Oracle AI Database documentation, reference content, and knowledge stores are structured for indexing, embedding, and retrieval by LLM-driven systems.
• Define and implement best practices for metadata, content taxonomies, canonical sources, schema/structured data, and machine-readable formatting to improve extraction and grounding.
• Develop strategies for improving semantic retrieval performance.

Prompt & Answer Quality Engineering

• Design prompt patterns and evaluation prompts to increase retrieval accuracy, coherence, and factuality, with a focus on reducing hallucinations and improving citation quality.
• Identify high-value prompts, topics, and query clusters where AI-generated answers underperform, and translate gaps into content + technical plans.

Measurement, Benchmarking & Experimentation

• Build AEO reporting and measurement frameworks using KPIs, including retrieval relevance, answer accuracy, citation quality, coverage, and latency.
• Run structured experiments (including A/B tests) on content formats and retrieval strategies; analyze outcomes and operationalize learnings into repeatable playbooks.
• Benchmark Oracle visibility and accuracy versus competitive narratives in AI-generated responses; track drift as AI models and ranking behaviors evolve.

Cross-Functional Execution

• Partner with Oracle AI Database Product, Engineering, and Product Marketing to implement changes such as markup, content restructuring, knowledge hub improvements, and publishing workflows that increase AI trust and retrieval performance.
• Create scalable AEO playbooks and enablement materials for technical product managers, including templates for Q&A, reference blocks, and “LLM-friendly” technical summaries.

Industry Monitoring

• Stay current with advances in LLM optimization, vector search, retrieval models, reranking, grounding/citations, and semantic discoverability; rapidly incorporate emerging best practices.

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Information Science, or related discipline.
• 1–3 years of experience in any of the following areas: ranking frameworks, forecasting models, search/retrieval optimization, information architecture, data/knowledge engineering, AI/ML implementation, technical documentation for complex systems.
• Knowledge of LLMs, prompt engineering, embeddings, and RAG architectures.
• Experience measuring and improving retrieval quality using metrics such as precision/recall, MRR, NDCG, and related evaluation methods.
• Strong skills in data analysis, scripting, and technical communication.
• Familiarity with Oracle AI Database, vector search, or comparable semantic retrieval technologies.

Preferred Qualifications

• Hands-on experience building or optimizing LLM-backed experiences, including content designed for grounded inference and citation-ready answers.
• Experience with neural networks, neural machine translation, or modern NLP architectures.
• Experience with relevance feedback loops, evaluation harnesses, and model quality monitoring in production settings.
• Background creating enterprise-grade knowledge bases, content hubs, or search/retrieval systems.
• Familiarity with tokenization and encoding methods

Disclaimer:

Certain US customer or client-facing roles may be required to comply with applicable requirements, such as immunization and occupational health mandates.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $91,100 to $199,500 per annum. May be eligible for bonus and equity.

Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.<