Principal Data Scientist

** Job Description*
* We are seeking an Applied Scientist to develop and product ionize 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.

** Responsibilities*
* 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 product ionize 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 MCPModel 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 work streams while seeking guidance on broader strategy and novel research directions.

Responsibilities

Applied AI Development

+ 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.

Customer & Product Partnership

+ 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.

Productionization & Operational Quality

+ 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.

Preferred Qualifications

+

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…