Data Scientist, Network Fabric Engineering

Amazon is hiring a Data Scientist, Network Fabric Engineering in Sydney, Australia. Level rates it ; you can apply on Amazon.

Level's read on this job

Works on AI ●●●○ is how Level rates this data scientist role: the daily work is on AI products, without building the model.
Tools and skills the posting cites include Python, Dashboards and data science.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Data scientist to design risk/reliability models and evaluate agentic automation systems for network fabric engineering.

PythonDashboards

data-sciencepredictive-modelingreliability-engineeringexperimentation

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.

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.

How we rate this

Data Scientist, Network Fabric Engineering at Amazon rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Data Scientist, Network Fabric Engineering is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in AU, NSW, Sydney.
The job was posted on June 26, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data SciencePredictive ModelingReliability EngineeringExperimentationPythonDashboards

Also common in data scientist jobs at this level

LLM Spark dbt Snowflake AI agents Databricks

Pay

Typical data scientist job: $168k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

Tell me about a project where data science was part of your work. What did you do?
Tell me about a project where predictive modeling was part of your work. What did you do?
Tell me about a project where reliability engineering was part of your work. What did you do?
Tell me about a project where experimentation was part of your work. What did you do?
Walk me through how you've used Python in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Data Science, Predictive Modeling, Reliability Engineering, Experimentation, and Python. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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