Data Scientist — Network Reliability & Predictive Analytics

Amazon Web Services (AWS); Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Amazon Web Services (AWS) in Sydney seeks a Data Scientist to define the data strategy for network reliability and automation initiatives. You will design metrics, build evidence, and measure outcomes to guide senior leadership decisions and AI-driven improvements across a massive global network.

The role embeds you with senior engineers to set analytical standards, develop risk models, and surface patterns that drive customer impact, enabling scalable, production-grade analytics and decision

Qualifications

• 2+ years of data scientist experience.
• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical software experience.
• 3+ years of machine learning/statistical modeling data analysis tools and techniques.
• 1+ years of working with or evaluating AI systems experience.

Responsibilities

• Define and drive the data science strategy for the program - metrics, experiments, and evidence.
• Lead the design and deployment of predictive risk and reliability models for network availability.
• Own the evidence behind program decisions and defend recommendations to senior audiences.
• Design and own the operational analytics and dashboards used by senior leadership to track network health.
• Design and run experiments to evaluate automation rollouts and measure impact on availability.
• Drive data quality improvements and event-driven scoring pipelines to support continuous improvement.

Skills

Data science

SQL

Python

Machine learning

Education

Bachelor's degree in STEM

Job description

Amazon Web Services (AWS) in Sydney seeks a Data Scientist to define the data strategy for network reliability and automation initiatives. You will design metrics, build evidence, and measure outcomes to guide senior leadership decisions and AI-driven improvements across a massive global network.

The role embeds you with senior engineers to set analytical standards, develop risk models, and surface patterns that drive customer impact, enabling scalable, production-grade analytics and decision