Applied Scientist 5

Oracle•Seattle, WA

About The Position

Key Responsibilities: Anomaly Detection: Research and develop advanced ML/AI algorithms for detecting, classifying, and prioritizing anomalies in real time across OCI’s global network infrastructure, ensuring rapid, automated incident identification. Demand Forecasting: Build and optimize predictive models for short-term (minute-level) network demand forecasting to enable dynamic control-loop actions. Develop long-term demand and capacity models to inform network expansion, planning, and business decisions. Triangulation, Correlation, and Alerting: Apply statistical and machine learning techniques to process high-volume telemetry, correlate symptoms and root causes, and enable the observability platform to triangulate problems down to specific devices, links, or regions. Design methodologies and set scientific thresholds for detection, alerting, and automated signal prioritization (not architecture or coding of the underlying platform). Observability Science: Define requirements and scientific advancements for data collection frequency, feature engineering, dimensionality reduction, and quality metrics—guiding engineering teams in building world-class observability and analytics. Collaboration & Influence: Work closely with engineering, network, and product teams, providing scientific guidance on feature design and system integration, and ensuring ML/AI insights are actionable in real-world operations. Knowledge Sharing: Author high-quality documentation and present findings, best practices, and research outcomes to both technical and non-technical stakeholders. Mentor peers on advanced analytics and networking science.

Requirements

PhD or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or related field focusing on machine learning, statistics, networking, or distributed systems.
5+ years of experience applying advanced ML, statistical modeling, or scientific analytics to real-world, large-scale technical systems.
Proven expertise in anomaly detection, time-series forecasting, and root-cause analysis, particularly in networking or distributed environment domains.
Proficiency in scientific computing and ML toolsets (e.g., Python, R, TensorFlow, PyTorch, Scikit-learn).
Deep understanding of methods for processing and extracting insights from high-volume telemetry or sensor data.

Nice To Haves

Experience defining scientific strategies for observability or incident management in large-scale networks or distributed systems.
Publications or patents in ML/AI applied to anomaly detection, forecasting, or similar areas.
Collaboration history with engineering teams to translate models into production environments.

Responsibilities

Anomaly Detection: Research and develop advanced ML/AI algorithms for detecting, classifying, and prioritizing anomalies in real time across OCI’s global network infrastructure, ensuring rapid, automated incident identification.
Demand Forecasting: Build and optimize predictive models for short-term (minute-level) network demand forecasting to enable dynamic control-loop actions. Develop long-term demand and capacity models to inform network expansion, planning, and business decisions.
Triangulation, Correlation, and Alerting: Apply statistical and machine learning techniques to process high-volume telemetry, correlate symptoms and root causes, and enable the observability platform to triangulate problems down to specific devices, links, or regions. Design methodologies and set scientific thresholds for detection, alerting, and automated signal prioritization (not architecture or coding of the underlying platform).
Observability Science: Define requirements and scientific advancements for data collection frequency, feature engineering, dimensionality reduction, and quality metrics—guiding engineering teams in building world-class observability and analytics.
Collaboration & Influence: Work closely with engineering, network, and product teams, providing scientific guidance on feature design and system integration, and ensuring ML/AI insights are actionable in real-world operations.
Knowledge Sharing: Author high-quality documentation and present findings, best practices, and research outcomes to both technical and non-technical stakeholders. Mentor peers on advanced analytics and networking science.

Benefits

Medical, dental, and vision insurance, including expert medical opinion
Short term disability and long term disability
Life insurance and AD&D
Supplemental life insurance (Employee/Spouse/Child)
Health care and dependent care Flexible Spending Accounts
Pre-tax commuter and parking benefits
401(k) Savings and Investment Plan with company match
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.
11 paid holidays
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.
Paid parental leave
Adoption assistance
Employee Stock Purchase Plan
Financial planning and group legal
Voluntary benefits including auto, homeowner and pet insurance