Senior Data Scientist

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.