About the job
At MYOB, we are not just a leading business management solution; we are committed to empowering businesses across Australia and New Zealand to thrive. Our core mission is to provide innovative tools that help businesses start, survive, and succeed.
We believe that when one business flourishes, it positively impacts the entire ecosystem, including owners, employees, customers, suppliers, and their communities. By joining MYOB, you will play an integral role in enabling businesses to operate smoothly and efficiently, allowing them to focus on what truly matters—achieving their goals.
As a part of MYOB, you will not only contribute to the success of our clients but will also grow alongside the communities we serve. While we specialize in software, our ultimate focus is on people—making MYOB truly Everyone’s Business.
About the Role
Join our dynamic Data and AI team to develop features that significantly enhance the experience of MYOB's customers.
As a Lead Data Scientist, you will navigate projects from conceptualization to production, utilizing a structured approach: prototype → MVP → scale. You will define clear metrics, conduct thorough offline testing, and implement live A/B experiments to validate your impact.
Collaborating closely with product managers, engineers, and designers, you will identify key challenges, select the appropriate methodologies (including transformers, recommenders, and time series analysis), and deliver scalable and maintainable AI solutions.
We seek a leader who embraces an experimentation-first mindset, advocates for responsible AI practices, and supports the growth of the team through mentorship and knowledge sharing.
The Skills You'll Need:
- Proven track record of delivering customer-facing ML/AI and LLM features from inception to deployment.
- Hands-on experience with NLP, RAG, time series analysis, recommendation systems, anomaly detection, and deep learning/transformers.
- Proficient in Python, with production experience in ML frameworks and LLM tools (e.g., HF Transformers, LangChain/LangGraph).
- Cloud-savvy, with familiarity in AWS services such as Bedrock, S3, SageMaker, ECS, and Lambda.
- Methodical in experimentation: adept in offline metrics, A/B testing, calibration/drift, human-in-the-loop strategies, and telemetry linked to actual outcomes.
- Solid foundation in data engineering principles, including SQL, NoSQL/vector stores, streaming, data quality/governance, and collaborative data contracts/observability.
- Deep understanding of security, privacy, and compliance, coupled with a passion for Responsible AI principles.
