Responsibilities
- Build and productionize models for network forecasting, capacity planning, and performance risk, including uncertainty and sensitivity analysis
- Design and maintain scalable datasets, feature pipelines, and monitoring systems to support modeling and decision workflows
- Develop and apply algorithms, analytical tooling, and approaches—including what-if analysis, optimization, anomaly/change detection, causal inference, prescriptive analytics, and simulation etc.—to support infrastructure planning and drive improvements across Meta’s global network
- Communicate results and recommendations through clear narratives, dashboards, and decision support tools
- Partner cross-functionally to translate network infrastructure needs into measurable metrics, model requirements, and shipped solutions
Minimum Qualifications
- Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
- 2+ years of experience applying statistical, machine learning, or optimization methods to real-world problems
- Programming skills in Python (or similar) and SQL, experience with large-scale data
- Experience delivering production-quality analytics/models including testing, code review, and scheduled pipelines or services
Preferred Qualifications
- Master’s or PhD in a quantitative field
- Experience with time-series forecasting for large-scale systems and uncertainty quantification
- Familiarity with networking fundamentals, or equivalent experience in large-scale infrastructure domains
- Proven track record in algorithm design and implementation for large-scale data, optimization, anomaly detection, or network infrastructure challenges
$122,000/year to $181,000/year + bonus + equity + benefits
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