Senior Data Scientist - h2o.ai
Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Oct 28, 2026.
H2O.ai is on a mission to democratize AI for Good. As the world’s leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built Agents, SLMs, and solutions on their private data. With a focus on secure, compliant, and infrastructure-flexible Sovereign AI deployments, H2O.ai delivers solutions that align with the highest standards of data privacy and control.
Its open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500. H2O.ai powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Wells Fargo, Bank of America, Workday, Progressive Insurance, and NIH.
For more information, visit www.h2o.ai.
About This Opportunity
We are looking for a Senior Data Scientist who thrives at the intersection of cutting-edge AI and real enterprise impact. You will work directly with some of APAC's most sophisticated organisations - helping them move from AI curiosity to AI value, across the full spectrum of predictive and generative AI.
This is not a lab role. You will be in the room with executives, data science teams, and business stakeholders - building solutions, running enablement programs, and translating complex AI into outcomes that decision-makers can act on. You will be backed by a world-class team including Kaggle Grandmasters, and equipped with H2O.ai's industry-leading platform spanning AutoML, LLM Studio, and agentic AI tooling.
This position is based in Sydney, Australia.
What You Will Do
- Partner with stakeholders to deliver data-driven AI/ML solutions across industries such as banking, insurance, retail, healthcare, and manufacturing.
- Build, validate, and deploy predictive ML models for forecasting, classification, optimisation, and anomaly detection.
- Apply statistical and ML techniques (regression, trees, ensembles, time series, clustering) based on business needs.
- Explore GenAI approaches where relevant, including LLMs, SLMs, RAG, prompt engineering, and basic agentic workflows.
- Perform end-to-end data science work including data exploration, feature engineering, modelling, evaluation, and interpretation.
- Translate model outputs into actionable insights for business stakeholders.
- Collaborate with engineering teams on model productionisation.
What We Are Looking For