Data Scientist Jobs in Sydney, USA

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.<br/><br/>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.<br/><br/>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.<br/><br/>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.<br/><br/>Key job responsibilities<br/>- Define and drive the data science strategy for the program — the metrics, the experiments, and what counts as evidence that a change worked<br/>- Lead the design and deployment of predictive risk and reliability models for network availability, using device failures, alarm telemetry, ticket data, and traffic signals<br/>- 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<br/>- 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<br/>- 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<br/>- Drive data quality and classification improvements — event categorisation, root-cause attribution — so the program's metrics rest on solid ground<br/>- Build and own event-driven scoring pipelines (Python, SQL, AWS Lambda, Amazon S3, Amazon Athena) that keep the decide / measure / improve loop running<br/>- 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<br/><br/>A day in the life<br/>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.<br/><br/>About the team<br/>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.

We’re a leading business management solution with a core purpose: helping more businesses in Australia and New Zealand start, survive and succeed.   At MYOB, we believe what’s good for one business is good for all business—and for all of us. Whether you support them, work for them, or dream of building your own, when businesses run smoothly, everybody feels it. Owners, employees, customers, suppliers—even families. That’s why we’re here: to give every person in business the tools they need to focus on what really matters and do Big Things—whatever big looks like for them.   And for you? Joining MYOB means being part of that impact. It means using your skills to help businesses thrive, shaping the future of work, and growing alongside the people and communities we support. Because while we’re the business of software, we’re really in the business of people. And that makes MYOB Everyone’s Business. About the role Join our Data and AI team and help build features that actually make a difference for MYOB's customers.   As a Data Scientist, you’ll take ideas from hypothesis all the way to production — prototype → MVP → scale — with clear metrics, robust offline testing, and live A/B experiments to prove impact.   You’ll work with product managers, engineers, and designers to frame the right problems, choose the right methods (think transformers, recommenders, time series), and deliver AI that’s scalable and maintainable.   We’re looking for someone who leads with an experiment-first mindset, champions responsible AI, and helps lift the whole team through mentoring and knowledge sharing.   The skills you'll need: - Consistent record of shipping customer-facing ML/AI and LLM features end-to-end. - Hands-on with NLP, RAG, time series, recommenders, anomaly detection and deep learning/transformers. - Strong Python skills, with production experience in ML frameworks and LLM tooling (HF Transformers, LangChain/LangGraph). - Cloud-ready, with experience across AWS services including Bedrock, S3, SageMaker, ECS and Lambda. - Experimentation-first approach: offline metrics, A/B tests, calibration/drift, human-in-the-loop, and telemetry tied to real outcomes. - Solid data engineering foundation: SQL, NoSQL/vector stores, streaming, data quality/governance, and collaborating on data contracts/observability. - Strong grounding in security, privacy and compliance, with a passion for Responsible AI principles.  

About Kasada Join us in stopping bad bots, for good! Kasada protects millions of online users everyday. Founded to stop automated bot attacks, we believe the internet should be a safe place for everyone. Bad bots are highly destructive. They take over accounts, steal content, overload systems and infrastructure and cause billions of dollars in damages every year. Seeking to restore trust in the internet, Kasada stops bots at the very first request including those that have never been seen before. We’ve grown from a few friends working out of a shipping container under the Sydney Harbour Bridge to now operating globally, we’re spread across the world protecting some of the most well-known brands on the face of the earth. We’re an innately curious team that’s not afraid to bring bold ideas to create better ways of solving problems. We’re looking for people who are passionate about solving some of the most difficult and pressing cybersecurity issues, while having fun doing it! Kasada has an exciting opportunity to join our team responsible for defending our customers against fraud and malicious automation. Attackers use a number of ever changing toolsets to launch highly costly attacks on our customers. As the first Data Scientist in the Account Intelligence team, you will work in a high energy team applying predictive modelling and statistical methods to help us defeat adversaries. A critical part of this role involves partnering with engineering, research, and security operations to turn data science work into production defences that stand up against real attackers. We are looking for a hands-on data scientist to help shape the predictive detection capabilities within Account Intelligence and the way we tell legitimate users apart from adversaries. While your focus may shift from time to time, you are expected to remain technical and hands on across the full model lifecycle, from exploring data and building models through to evaluating how your work performs once adversaries start adapting. You will be supported by experienced engineering and research teams to take your work from notebook to production.