Senior Manager, Applied Science, Network Planning Solutions

Key job responsibilities

• Lead AI/ML Innovation For Network Planning Solutions
• Develop and deploy production-ready demand forecasting algorithms that continuously sense and predict customer demand using real-time signals
• Build network optimization algorithms that automatically adjust staffing as conditions evolve across the service network
• Architect scalable AI/ML infrastructure supporting automated forecasting and network optimization capabilities across the system
• Drive Scientific Excellence
• Build and mentor a team of applied scientists to deliver breakthrough AI/ML solutions
• Design rigorous experiments to validate hypotheses and quantify business impact
• Establish scientific excellence mechanisms including evaluation metrics and peer review processes
• Enable Strategic Transformation
• Drive scientific innovation from research to production - Design and validate next-generation AI-native models while ensuring robust performance, explainability, and seamless integration with existing systems
• Partner with Engineering, Product, and Operations teams to translate AI/ML capabilities into measurable business outcomes
• Navigate ambiguity through experimentation while balancing innovation with operational constraints
• Influence senior leadership through scientific rigor, translating complex algorithms into clear business value

About The Team

We are Network Planning Solutions, a team of scientific innovators dedicated to reshaping how global service networks operate. Our mission is to create AI-native solutions that continuously learn, predict, and optimize customer experiences. We empower our associates to tackle high-value challenges and drive transformative change at a global scale.

Basic Qualifications

• 10+ years of building large-scale machine learning and AI solutions at Internet scale
• Master's degree in Computer Science (Machine Learning, AI, Statistics, or equivalent)
• Experience building large-scale machine learning and AI solutions at Internet scale
• Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track

Preferred Qualifications

• 10+ years of practical work applying ML to solve complex problems for large-scale applications
• 5+ years of hands-on work in big data, machine learning and predictive modeling
• 5+ years of people management experience
• PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
• Experience in practical work applying ML to solve complex problems for large scale applications
• Experience developing, deploying and managing AI products at scale
• Experience building complex highly-scalable systems that involve predictive models or applications of machine learning