Data Scientist, Network Fabric Engineering at Amazon Web Services

Amazon Web Services; Sydney; On-site; AUD 120,000 - 170,000; Full time

Job summary

Amazon Web Services in Sydney is seeking a Data Scientist to drive the data science strategy behind a major network modernization. You will define metrics, own evidence, and measure outcomes at scale, embedding your voice among senior engineers.

You will shape dashboards and models to surface customer-impact patterns, drive automation initiatives, and ensure data quality across millions of devices and events.

Qualifications

• 2+ years of data scientist experience.
• 3+ years of SQL, scripting (Python) or statistical software.
• 3+ years of ML/statistical modeling experience and related tools.
• 1+ years working with AI systems.

Responsibilities

• Define and drive the data science strategy, metrics and experiments.
• Lead predictive risk and reliability models for network availability.
• Own the evidence behind program decisions and their impact.
• Present recommendations to senior technical and business leaders.
• Design and own dashboards using QuickSight, CloudWatch, and Python.
• Design and run experiments evaluating automation including agentic systems.
• Improve data quality and event classification for reliable metrics.
• Build event-driven scoring pipelines using Python, SQL, Lambda, S3, Athena.
• Collaborate with engineers to raise the bar on evidence-based decisions.

Skills

Data science

SQL

Python

Machine learning

AI systems

Education

Bachelor’s degree in STEM

Tools

Amazon QuickSight

CloudWatch

AWS Lambda

S3

Athena

Job description

DESCRIPTION

AWS Networking operates one of the largest and most complex networks on the planet. The team you'd join is responsible for the availability of that network — measuring how it performs for customers, predicting where it is most likely to degrade, and reshaping how we operate it as the workload grows.

This Full time on site position offers great opportunities for career growth.

We are in the middle of a significant change in how network operations are run. Lessons from our recent work on automation, AI, and ML — including agentic systems that triage and mitigate incidents alongside engineers — are feeding into a broader rethink of where humans focus, where automation takes over, and how we measure whether either is working.

We are looking for a Data Scientist to join the team in Sydney to drive the data science strategy behind that change. You will define the metrics that matter, own the evidence the team uses to make decisions, and measure whether each decision delivered the outcomes we expected.

You’ll be the data science voice on a team of senior network and software engineers — the person who decides what we measure, how we measure it, and what the numbers actually mean.

Concretely, that means setting the analytical bar for the program, designing risk and reliability models against telemetry from millions of network devices, surfacing the patterns that drive customer‑impact incidents, and turning that analysis into the dashboards and metrics our leaders use to set priorities.

It also means owning the evaluations that tell us when a new piece of automation — including the agents we are rolling out to support engineers on the front line — is actually moving the needle on availability, and not just adding noise.

If you are a scientist who wants to shape how a tier‑one production network is run — using data to drive program strategy, not just to support it — at a scale no academic lab or startup can match, and you’re at your best as the data science voice embedded in a team of engineers, this is the team for you.

Key job responsibilities

• Define and drive the data science strategy for the program — the metrics, the experiments, and what counts as evidence that a change worked
• Lead the design and deployment of predictive risk and reliability models for network availability, using device failures, alarm telemetry, ticket data, and traffic signals
• Own the evidence behind program decisions: where availability is at risk, where automation is ready to expand, where engineering effort has the highest leverage
• Defend recommendations to senior technical and business audiences
• Design and own the operational analytics and dashboards (Amazon QuickSight, Amazon CloudWatch, Python) used by senior leadership to track network health and the impact of operational change
• Design and run experiments to evaluate the automation we are rolling out — including agentic systems supporting engineers on incidents — measuring whether each rollout improved availability
• Drive data quality and classification improvements — event categorisation, root‑cause attribution — so the program’s metrics rest on solid ground
• Build and own event‑driven scoring pipelines (Python, SQL, AWS Lambda, Amazon S3, Amazon Athena) that keep the decide / measure / improve loop running
• Bring statistical rigour to the engineers you partner with — review experiment designs, push back on unsupported assumptions, and raise the bar on how the team uses evidence

A day in the life

You might start the morning defining how the team will measure a new initiative — the success metrics, the counterfactual, the bar for calling it a win. By mid‑morning you're with the engineering team turning a proposal into a decision: walking through trade‑offs, pushing back where the data doesn’t support an assumption.

The afternoon is outcome measurement — refining the evaluation pipeline that tracks last week’s rollout, updating the CloudWatch dashboard senior leadership uses to gate the next expansion, and prepping the data for an upcoming Director review.

About the team

We sit inside AWS Networking with a strong Sydney presence and a remit that spans network availability, the data and analytics that support it, and the automation we are building to change how operations are done.

You’d be the data science voice in a small, senior team of network and software engineers in Sydney, partnering with the broader network engineering organisation across Seattle and Dublin.

Small team, high autonomy, direct line to senior leadership, and a roadmap with real production impact rather than research demos.

BASIC QUALIFICATIONS

• 2+ years of data scientist experience
• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• 1+ years of working with or evaluating AI systems experience

PREFERRED QUALIFICATIONS

• Bachelor’s degree or above in Science, Technology, Engineering, or Mathematics (STEM)
• Knowledge of machine learning concepts and their application to reasoning and problem‑solving
• Experience in Python, Perl, or another scripting language
• Experience in a ML or data scientist role with a large technology company
• Experience in defining and creating benchmarks for assessing GenAI model performance

Acknowledgement of country

In the spirit of reconciliation Amazon acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.

IDE statement

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.