Sr. Principal Applied Scientist
Oracle
Sr. Principal Applied Scientist
Job Description
At Oracle Cloud Infrastructure (OCI), we build the future of the cloud for enterprises as a diverse team of creators and inventors. We combine the speed and mindset of a start-up with the scale, security, and customer focus of a leading enterprise software company. OCI is seeking an exceptional Senior Principal Applied Scientist to develop AI-enabled platform capabilities for scientific and data-intensive enterprise use cases. This role will begin by addressing the needs of a large strategic customer and will evolve those learnings into scalable, reusable OCI platform capabilities for a broader customer base. You will work at the intersection of applied natural sciences, data science, and AI engineering. You will partner closely with product management, customers, applied scientists, and software engineers to define problems, develop and evaluate solutions, and bring high-quality capabilities into production. This is an ideal role for a scientist who is comfortable operating in a fast-paced environment, iterating quickly as customer needs evolve, and translating scientific domain knowledge into practical cloud and AI products. As a Senior Principal Applied Scientist, you will provide technical leadership for complex, high-impact initiatives. You will establish scientific and technical direction, lead research and applied-development efforts, influence architecture and product strategy, and mentor other scientists and engineers. Career Level — IC5 What You Will BringYou are an applied scientist with strong scientific judgment, practical technical depth, and a customer-oriented mindset. You can move comfortably between scientific inquiry and product delivery: understanding a customer’s immediate problem, designing rigorous experiments, working with engineers to build a solution, and shaping that solution into a platform capability that can serve many customers. You are energized by ambiguity, comfortable iterating quickly, and focused on delivering measurable value. ResponsibilitiesLead research, experimentation, and applied development for AI-enabled scientific-data and enterprise platform capabilities.Work directly with product managers and strategic customers to understand scientific workflows, data challenges, and evolving requirements.Rapidly prototype, evaluate, and refine solutions; make informed tradeoffs and pivot approach when evidence or customer needs warrant a change.Develop customer-focused capabilities with a deliberate path toward reusable, configurable OCI platform services.Apply expertise in natural sciences, such as molecular biology or related disciplines, to frame meaningful scientific problems, validate solution quality, and ensure domain relevance.Design and evaluate data-science, AI, and data-engineering approaches for structured, unstructured, multimodal, and scientific datasets.Develop and assess AI solutions, including retrieval-augmented generation, data enrichment, knowledge extraction, model evaluation, AI agents, and AI-assisted scientific workflows, where applicable.Define rigorous evaluation methods, benchmarks, quality metrics, and validation processes for models and end-to-end platform capabilities.Collaborate with software engineers to operationalize models, data pipelines, evaluation harnesses, and AI integrations into secure, scalable, production-grade services.Drive technical design reviews and influence product and platform decisions across science, engineering, and product organizations.Identify emerging methods, tools, datasets, and technologies that can improve OCI’s scientific AI and data capabilities.Mentor applied scientists and engineers; raise the bar for scientific rigor, experimentation, documentation, and technical execution.Communicate technical findings, risks, tradeoffs, and recommendations clearly to technical and non-technical stakeholders.Qualifications and Experience PhD in molecular biology, bioinformatics, computational biology, chemistry, physics, engineering, computer science, data science, or a related quantitative scientific discipline; or a Master’s degree with 8+ years of relevant industry or research experience.Demonstrated expertise in an applied natural-science domain, such as molecular biology, genomics, bioinformatics, life sciences, or a comparable scientific field.Significant hands-on experience applying data science, machine learning, AI integration, data engineering, or analytics to real-world scientific or enterprise problems.Proven ability to lead complex research-and-development initiatives from problem definition through implementation, evaluation, and delivery.Experience working in an iterative, customer-driven environment with changing requirements and short feedback cycles.Strong knowledge of experimental design, statistical analysis, data quality, model validation, and quantitative evaluation.Experience with Python and common scientific, data-science, or machine-learning libraries and tooling.Ability to work effectively across product management, engineering, science, and customer teams.Strong written and verbal communication skills, including the ability to explain complex scientific and technical concepts clearly.Demonstrated technical leadership and a commitment to mentoring others.Preferred Qualifications Experience applying generative AI, large language models, retrieval-augmented generation, AI agents, or knowledge systems to scientific or data-intensive workflows.Experience with scientific-data platforms, data pipelines, data governance, ontology/knowledge-graph systems, or cloud-based analytics.Experience integrating AI models into enterprise applications or production cloud services.Familiarity with model evaluation harnesses, benchmarking, responsible AI practices, and AI quality or safety assessment.Publications, patents, open-source contributions, or a record of innovation in relevant scientific, AI, or data-science fields.Experience partnering directly with large enterprise customers to define, validate, and deploy technical solutions.Experience with OCI or another major cloud platform. 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 onlyUS: Hiring Range in USD from: $158,300 to $355,400 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 opinion2. Short term disability and long term disability3. Life insurance and AD&D4. Supplemental life insurance (Employee/Spouse/Child)5. Health care and dependent care Flexible Spending Accounts6. Pre-tax commuter and parking benefits7. 401(k) Savings and Investment Plan with company match8. 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 holidays10. 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 leave12. Adoption assistance13. Employee Stock Purchase Plan14. Financial planning and group legal15. Voluntary benefits including auto, homeowner and pet insuranceThe 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 - IC5
Locations
- Nashville, TN, United States
- United States
Salary
158,300 - 355,400 USD / yearly
Skills Required
- applied natural-science domainintermediate
- experimental designintermediate
- Pythonintermediate
- scientific-data platformsintermediate
- model evaluation harnessesintermediate
- OCIintermediate
Required Qualifications
- PhD in molecular biology, bioinformatics, computational biology, chemistry, physics, engineering, computer science, data science, or a related quantitative scientific discipline; or a Master’s degree with 8+ years of relevant industry or research experience. (experience, 8 years)
- Demonstrated expertise in an applied natural-science domain, such as molecular biology, genomics, bioinformatics, life sciences, or a comparable scientific field. (experience)
- Significant hands-on experience applying data science, machine learning, AI integration, data engineering, or analytics to real-world scientific or enterprise problems. (experience)
- Proven ability to lead complex research-and-development initiatives from problem definition through implementation, evaluation, and delivery. (experience)
- Experience working in an iterative, customer-driven environment with changing requirements and short feedback cycles. (experience)
- Strong knowledge of experimental design, statistical analysis, data quality, model validation, and quantitative evaluation. (experience)
- Experience with Python and common scientific, data-science, or machine-learning libraries and tooling. (experience)
- Ability to work effectively across product management, engineering, science, and customer teams. (experience)
- Strong written and verbal communication skills, including the ability to explain complex scientific and technical concepts clearly. (experience)
- Demonstrated technical leadership and a commitment to mentoring others. (experience)
- PhD in molecular biology, bioinformatics, computational biology, chemistry, physics, engineering, computer science, data science, or a related quantitative scientific discipline; or a Master’s degree with 8+ years of relevant industry or research experience. (experience, 8 years)
- Demonstrated expertise in an applied natural-science domain, such as molecular biology, genomics, bioinformatics, life sciences, or a comparable scientific field. (experience)
- Significant hands-on experience applying data science, machine learning, AI integration, data engineering, or analytics to real-world scientific or enterprise problems. (experience)
- Proven ability to lead complex research-and-development initiatives from problem definition through implementation, evaluation, and delivery. (experience)
- Experience working in an iterative, customer-driven environment with changing requirements and short feedback cycles. (experience)
- Strong knowledge of experimental design, statistical analysis, data quality, model validation, and quantitative evaluation. (experience)
- Experience with Python and common scientific, data-science, or machine-learning libraries and tooling. (experience)
- Ability to work effectively across product management, engineering, science, and customer teams. (experience)
- Strong written and verbal communication skills, including the ability to explain complex scientific and technical concepts clearly. (experience)
- Demonstrated technical leadership and a commitment to mentoring others. (experience)
Preferred Qualifications
- Experience applying generative AI, large language models, retrieval-augmented generation, AI agents, or knowledge systems to scientific or data-intensive workflows. (experience)
- Experience with scientific-data platforms, data pipelines, data governance, ontology/knowledge-graph systems, or cloud-based analytics. (experience)
- Experience integrating AI models into enterprise applications or production cloud services. (experience)
- Familiarity with model evaluation harnesses, benchmarking, responsible AI practices, and AI quality or safety assessment. (experience)
- Publications, patents, open-source contributions, or a record of innovation in relevant scientific, AI, or data-science fields. (experience)
- Experience partnering directly with large enterprise customers to define, validate, and deploy technical solutions. (experience)
- Experience with OCI or another major cloud platform. (experience)
- Experience applying generative AI, large language models, retrieval-augmented generation, AI agents, or knowledge systems to scientific or data-intensive workflows. (experience)
- Experience with scientific-data platforms, data pipelines, data governance, ontology/knowledge-graph systems, or cloud-based analytics. (experience)
- Experience integrating AI models into enterprise applications or production cloud services. (experience)
- Familiarity with model evaluation harnesses, benchmarking, responsible AI practices, and AI quality or safety assessment. (experience)
- Publications, patents, open-source contributions, or a record of innovation in relevant scientific, AI, or data-science fields. (experience)
- Experience partnering directly with large enterprise customers to define, validate, and deploy technical solutions. (experience)
- Experience with OCI or another major cloud platform. (experience)
Responsibilities
- Lead research, experimentation, and applied development for AI-enabled scientific-data and enterprise platform capabilities.
- Work directly with product managers and strategic customers to understand scientific workflows, data challenges, and evolving requirements.
- Rapidly prototype, evaluate, and refine solutions; make informed tradeoffs and pivot approach when evidence or customer needs warrant a change.
- Develop customer-focused capabilities with a deliberate path toward reusable, configurable OCI platform services.
- Apply expertise in natural sciences, such as molecular biology or related disciplines, to frame meaningful scientific problems, validate solution quality, and ensure domain relevance.
- Design and evaluate data-science, AI, and data-engineering approaches for structured, unstructured, multimodal, and scientific datasets.
- Develop and assess AI solutions, including retrieval-augmented generation, data enrichment, knowledge extraction, model evaluation, AI agents, and AI-assisted scientific workflows, where applicable.
- Define rigorous evaluation methods, benchmarks, quality metrics, and validation processes for models and end-to-end platform capabilities.
- Collaborate with software engineers to operationalize models, data pipelines, evaluation harnesses, and AI integrations into secure, scalable, production-grade services.
- Drive technical design reviews and influence product and platform decisions across science, engineering, and product organizations.
- Identify emerging methods, tools, datasets, and technologies that can improve OCI’s scientific AI and data capabilities.
- Mentor applied scientists and engineers; raise the bar for scientific rigor, experimentation, documentation, and technical execution.
- Communicate technical findings, risks, tradeoffs, and recommendations clearly to technical and non-technical stakeholders.
- Lead research, experimentation, and applied development for AI-enabled scientific-data and enterprise platform capabilities.
- Work directly with product managers and strategic customers to understand scientific workflows, data challenges, and evolving requirements.
- Rapidly prototype, evaluate, and refine solutions; make informed tradeoffs and pivot approach when evidence or customer needs warrant a change.
- Develop customer-focused capabilities with a deliberate path toward reusable, configurable OCI platform services.
- Apply expertise in natural sciences, such as molecular biology or related disciplines, to frame meaningful scientific problems, validate solution quality, and ensure domain relevance.
- Design and evaluate data-science, AI, and data-engineering approaches for structured, unstructured, multimodal, and scientific datasets.
- Develop and assess AI solutions, including retrieval-augmented generation, data enrichment, knowledge extraction, model evaluation, AI agents, and AI-assisted scientific workflows, where applicable.
- Define rigorous evaluation methods, benchmarks, quality metrics, and validation processes for models and end-to-end platform capabilities.
- Collaborate with software engineers to operationalize models, data pipelines, evaluation harnesses, and AI integrations into secure, scalable, production-grade services.
- Drive technical design reviews and influence product and platform decisions across science, engineering, and product organizations.
- Identify emerging methods, tools, datasets, and technologies that can improve OCI’s scientific AI and data capabilities.
- Mentor applied scientists and engineers; raise the bar for scientific rigor, experimentation, documentation, and technical execution.
- Communicate technical findings, risks, tradeoffs, and recommendations clearly to technical and non-technical stakeholders.
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