Lead Principal Product Manager

Oracle•San Francisco, CA

8d•$119,200 - $264,100

About The Position

Oracle Cloud Infrastructure (OCI) is developing scientific platform capabilities for genomics, pathogen research, and other computationally intensive scientific workflows. The Senior Principal Product Manager will define product direction and deliver capabilities that address strategic customer needs while establishing a foundation for broader adoption. This is a senior individual contributor role owning product strategy, customer discovery, roadmap, and product outcomes for scientific computing and genomics capabilities. The role works directly with scientists, research computing teams, engineering, and applied science to translate complex scientific workflows into reliable, usable cloud capabilities. It operates independently across teams and guides product decisions from initial requirements through launch, adoption, and commercial validation.

Requirements

Bachelor’s degree in Computer Science, Engineering, Natural Sciences, Business, or a related discipline, or equivalent practical experience.
10+ years of relevant product management experience, including substantial ownership of highly technical enterprise software, cloud, data, or platform products.
Ability to independently lead product strategy and delivery across multiple teams, from ambiguous customer needs through launch and measurable adoption.
Experience working directly with technically sophisticated users and customers on scientific computing, data-intensive workflows, AI platforms, or comparable complex technical environments.
Strong understanding of cloud infrastructure, distributed systems, APIs, data platforms, identity, and enterprise integration, with ability to evaluate scientific workflow and operational tradeoffs with engineers and architects.
Experience defining business cases, prioritizing investment, and using qualitative research and quantitative evidence to make product decisions.
Excellent writing, executive communication, and ability to influence across organizations.

Nice To Haves

Experience in genomics, bioinformatics, computational biology, pathogen research, scientific computing, or life sciences software, including direct engagement with researchers.
Familiarity with established scientific workflow engines, containerized execution, scientific interfaces, and reproducible computational environments.
Experience integrating research data, scientific applications, laboratory informatics, simulation tools, or computational workflows with cloud services.
Experience with AI-enabled scientific applications, model evaluation, agents, and production cloud products, including commercialization and customer adoption.

Responsibilities

Define product vision, strategy, roadmap, and investment recommendations for scientific computing and genomics, prioritizing customer outcomes and reusable capabilities.
Build relationships with scientists, bioinformaticians, research computing teams, and customer decision makers; understand their objectives, pipelines, data flows, tools, and operational constraints.
Translate customer discovery into requirements, user journeys, prioritized capabilities, release objectives, and acceptance criteria; own decisions from discovery through development, pilot deployment, launch, and continuous improvement.
Define the experience for onboarding, configuring, executing, monitoring, and troubleshooting scientific computational workflows, addressing identity, access, execution status, results, reproducibility, provenance, and cost visibility.
Partner with engineering and architecture on integrations with workflow engines, scientific applications, data systems, and cloud infrastructure; make tradeoffs across usability, performance, reliability, security, interoperability, cost, and delivery time.
Coordinate with the product owner for laboratory automation and robotics on workflows spanning computational and physical experimentation; define shared requirements for execution context, evidence, and continuity without duplicating other systems' capabilities.
Work with applied science and engineering to identify valuable AI capabilities, scientific evaluation needs, and agent interactions; define measures of correctness, reproducibility, user benefit, and appropriate human oversight.
Develop market and competitive insight, positioning, business cases, and adoption hypotheses; partner with commercial teams on packaging, pricing recommendations, customer enablement, and expansion economics.
Establish and use measures for adoption, time to first successful workflow, onboarding effort, execution reliability, reproducibility, computational cost, and scientific workflow efficiency; revise roadmap and investment recommendations using evidence.
Represent product direction to executives, customers, and partners; resolve complex cross-team product issues, mentor product colleagues, and establish effective discovery and decision practices through influence and technical credibility.