Senior Data Scientist

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.

• 4+ years of experience in data science, machine learning, or applied AI roles.
• Strong predictive ML foundations (feature engineering, model selection, evaluation, interpretability).
• Experience working with real-world datasets and business problem framing.
• Exposure to GenAI: LLMs, SLMs, RAG, prompt engineering, or agentic workflows.
• Strong Python skills.
• Understanding of deployment/MLOps concepts.
• Strong communication, storytelling and presentation skills.

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