Principal Data Scientist at Oracle in Location not listed

Principal Data Scientist

Location: United States

We are seeking an Applied Scientist to develop and productionize AI capabilities supporting a strategic enterprise customer engagement. The team is initially focused on understanding and solving high-value customer needs in a scientific-labs setting, while creating reusable AI platform capabilities that can scale to broader OCI customers.
In this role, you will work closely with software engineers, product managers, customer technical teams, and other scientists to turn customer workflows and business problems into practical AI solutions. You will contribute to experimentation, model evaluation, data analysis, prototype development, and production deployment across areas such as generative AI, retrieval-augmented generation (RAG), agentic systems, model training, and model evaluation.

What You’ll Do

• Develop, evaluate, and improve applied AI and machine-learning solutions for customer and platform use cases.

• Work with product and customer teams to understand domain workflows, user needs, data characteristics, and success criteria.

• Design and execute experiments to assess model quality, reliability, latency, cost, safety, and customer value.

• Build prototypes and proof-of-concepts that validate technical approaches and inform product decisions.

• Develop model-evaluation frameworks, test sets, benchmarks, and measurement approaches for AI capabilities.

• Contribute to RAG, agentic AI, model-training, model-serving, or Model Context Protocol (MCP) based solutions.

• Partner with software engineers to productionize models, prompts, pipelines, evaluation harnesses, and AI-service integrations.

• Analyze model behavior, customer feedback, and product telemetry to identify quality gaps and recommend improvements.

• Help define data requirements, data-preparation approaches, and responsible-AI considerations for supported use cases.

• Contribute to technical documentation, design reviews, knowledge sharing, and scientific best practices.

• Stay current with advances in machine learning, generative AI, AI agents, evaluation methods, and cloud AI platforms.

• What You’ll Bring
  • 5+ years of relevant industry, research, or applied-science experience, or an advanced degree with relevant practical experience.
  • Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Statistics, Applied Mathematics, Physics, Engineering, Natural Sciences, or a related discipline; equivalent experience will be considered.
  • Experience applying machine learning, statistical modeling, data science, or generative AI to real-world problems.
  • Strong programming skills in Python and familiarity with common data-science and machine-learning libraries.
  • Experience with one or more of the following:
  Generative AI, large language models, prompt engineering, RAG, AI agents, or MCP
  Model training, fine-tuning, inference, evaluation, benchmarking, or model harnesses
  Data engineering, data platforms, data pipelines, or large-scale data analysis
  Applied natural-sciences research, scientific computing, laboratory systems, or research-data workflows
  • Ability to design structured experiments, interpret results, and communicate recommendations clearly.
  • Experience collaborating with software engineers and product managers to deliver practical, production-ready solutions.
  • Ability to independently own moderately complex scientific workstreams while seeking guidance on broader strategy and novel research directions.
  

• Develop and evaluate machine-learning and generative-AI approaches for defined customer and platform problems.

• Build model prototypes, experiments, and evaluation harnesses.

• Improve AI quality, accuracy, relevance, reliability, latency, and cost through disciplined experimentation.

• Apply appropriate methods for data preparation, validation, testing, and model assessment.

• Work with product managers and customer stakeholders to understand use cases and translate them into measurable AI objectives.

• Incorporate customer feedback into model evaluation and iterative solution improvements.

• Help distinguish customer-specific needs from reusable AI platform capabilities.

• Clearly communicate experimental findings, tradeoffs, limitations, and recommended next steps.

• Partner with engineering teams to integrate models and AI workflows into secure, scalable cloud services.

• Contribute to monitoring and evaluation approaches for production AI behavior and quality.

• Help identify and mitigate risks related to model reliability, data quality, safety, privacy, and security.

• Support incident analysis and continuous improvement for deployed AI capabilities.

• Experience with OCI Generative AI, Oracle Cloud Infrastructure, or another major cloud AI platform.

• Experience with model-serving frameworks, vector databases, embedding models, orchestration frameworks, or AI-agent tooling.

• Experience evaluating LLM-based systems for correctness, groundedness, safety, latency, and cost.

• Experience with scientific research organizations, laboratory environments, regulated industries, or complex enterprise data.

• Publications, patents, open-source contributions, or demonstrated technical leadership in applied AI, machine learning, or data science.

Disclaimer:

Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements.

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

US: Hiring Range in USD from: $114,600 to $234,600 per annum. May be eligible for bonus, equity, and compensation deferral.


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.

Oracle US offers a comprehensive benefits package which includes the following:
1. Medical, dental, and vision insurance, including expert medical opinion
2. Short term disability and long term disability
3. Life insurance and AD&D
4. Supplemental life insurance (Employee/Spouse/Child)
5. Health care and dependent care Flexible Spending Accounts
6. Pre-tax commuter and parking benefits
7. 401(k) Savings and Investment Plan with company match
8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
9. 11 paid holidays
10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
11. Paid parental leave
12. Adoption assistance
13. Employee Stock Purchase Plan
14. Financial planning and group legal
15. Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.

Career Level - IC4

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