Senior Network Analytics Engineer (Remote)

Job summary

This senior position focuses on directing optimization and modeling efforts to enhance network planning and design. The role involves defining problem structures, designing optimization models, and leading new capabilities from concept to implementation. The engineer will work on complex routing and network design challenges, utilizing strong analytical skills and engineering practices.

Qualifications

• Master’s degree or PhD in engineering, mathematics, computer science, or a related quantitative field with experience in optimization or modeling.
• Proven experience in designing and deploying optimization models or decision support algorithms in operational settings.
• Expert-level proficiency in Python, experience with C++ for performance-critical components, and strong SQL skills.
• Advanced knowledge of Operations Research, including Mixed Integer Programming and large-scale optimization techniques.

Responsibilities

• Design and implement advanced optimization models and heuristics.
• Frame complex planning problems with clear mathematical formulations.
• Develop reusable modeling components that integrate with production data systems.
• Enhance existing optimization logic for improved performance and scalability.
• Conduct structured experiments to validate model behavior with data.
• Collaborate with stakeholders to translate findings into actionable recommendations.
• Mentor junior analysts and promote high standards in modeling practices.

Skills

• Strong analytical judgment and technical expertise in optimization solutions.
• Ability to communicate effectively with both technical and non-technical audiences.
• Proficient in Git and modern software engineering workflows.
• Capable of leading change and guiding stakeholders through model adoption.

Tools

It is a plus if you also bring:

• Python, C++, SQL, Git, and optimization software tools.
• Familiarity with GPU acceleration or parallel computation.
• Experience with AI or machine learning methods.
• Knowledge of deep learning architectures and statistical inference.