Lead Principal Applied Scientist

Oracle•United States,

19h•$166,200 - $355,400

About The Position

Oracle’s computer vision team is responsible for growing its capabilities in image analysis and recognition, helping customers apply computer vision to business problems. The Lead Principal Applied Scientist will spearhead computer vision initiatives, lead the team’s technical direction, and drive the development and implementation of advanced computer vision technologies. The role works with applied scientists, engineers, cross-functional teams, customers, and external research partners to deliver high-quality models and services and develop real-world solutions. The position leads research and development, solution delivery, and the computer vision roadmap. It guides technical teams through the development, integration, deployment, and review of solutions; establishes research and engineering practices; communicates research findings; and builds the team’s research capabilities and external presence. It also provides strategic project leadership, addresses complex problems, supports talent development, and promotes continuous learning and improvement.

Requirements

PhD in Computer Science, Electrical Engineering, or a related field, with a dissertation, thesis, or final project centered on computer vision and machine learning, plus 8 or more years of relevant experience; alternatively, a master’s or bachelor’s degree in a related field with 10 or more years of relevant experience. The posting states the PhD path is preferred but not required.
Extensive experience in computer vision, deep learning, and image processing.
Strong understanding of machine learning algorithms and architectures.
Proven track record of leading research and development projects.
Strong publication record, including experience as a lead author or reviewer in top-tier journals or conferences.
Excellent problem-solving and analytical skills.
Strong leadership and communication abilities.

Nice To Haves

PhD in Computer Science, Electrical Engineering, or a related field with a computer vision and machine learning-focused dissertation, thesis, or final project, and 8 or more years of relevant experience; this path is preferred but not required.

Responsibilities

Lead and mentor a high-performing team of scientists and provide technical direction for computer vision initiatives.
Work with cross-functional teams to integrate computer vision capabilities into applications and products.
Identify opportunities involving computer vision and emerging technologies, and drive development of the computer vision roadmap.
Extract meaningful insights from complex data and metadata, interpret findings, and communicate them across Oracle.
Lead creation of patents, white papers, and other technical documentation related to applied research.
Evaluate high-complexity code, maintain and organize the codebase, establish version-control, code-review, and delivery practices, and own the team repository.
Build, maintain, and review documentation for experimentation, data collection, analysis, and model building.
Guide technical teams in delivering and integrating solutions on schedule, including collaboration with software and machine learning engineers on production deployment.
Lead peer review and coordinate solution delivery with applied science team members, ensuring solutions address product and business problems.
Define operational and user-feedback metrics, analyze results to inform technical strategy, and partner with operational teams to monitor production solutions.
Demonstrate research expertise and thought leadership, applying domain knowledge and industry best practices and guiding others in their use.
Lead publication, presentation, and review of research findings; serve as a keynote speaker; participate in scientific peer review; and establish external research collaborations where appropriate.
Manage timelines, deliverables, and budgets when applicable for critical projects, and adapt plans to changing business priorities.
Influence cross-functional leaders and external stakeholders, foster strategic partnerships, and incorporate diverse perspectives.
Lead advanced problem-solving efforts, guide data-driven approaches to ambiguous issues, identify root causes, and implement solutions that prevent recurrence.
Monitor emerging trends, technologies, and standards; contribute to the field through thought leadership; and promote learning, knowledge sharing, and professional development.
Develop and implement process improvements, evaluate their effectiveness, and establish metrics to measure success.
Advise on talent needs and organizational talent strategy, and contribute expertise to interviews, candidate assessments, and hiring decisions.