About Rhino Partners / Get AI Ready
Rhino Partners Limited, trading as Get AI Ready, is a specialist artificial intelligence, data science, analytics and digital-transformation consultancy delivering enterprise-grade machine learning, advanced analytics and AI solutions for Australian and international clients across multiple industries.
Our client portfolio spans financial services, banking, energy infrastructure, healthcare, telecommunications, insurance, retail and government sectors across Australia, Southeast Asia, New Zealand, Japan and North America. We are a recognised Databricks delivery partner and are currently engaged in the development of one of Australia’s first grounded clinical LLM-RAG applications for Australian General Practitioners, Primary Health Networks (PHNs) and government/medical academia.
We are seeking a Lead AI Scientist to anchor our technical delivery capability and lead complex data science and applied-AI initiatives across our enterprise client engagements.
Why This Position Exists
Our client pipeline has expanded significantly across the Australian, New Zealand and wider Asia-Pacific markets, with concurrent active programmes spanning regulated healthcare AI (Australian primary care), energy-infrastructure predictive maintenance, banking and financial-services analytics, telecommunications customer analytics, and government decision-intelligence. Several of these programmes run simultaneously, each with distinct regulatory regimes, data environments and executive stakeholders.
Delivering this portfolio requires a senior practitioner who can independently lead end-to-end model development, deploy and govern production ML/AI and Generative-AI systems, manage data obligations across multiple jurisdictions, and advise client executives and government bodies directly. This depth and breadth requires a proven multi-sector, multi-jurisdiction practitioner-leader.
This is not a general analyst, BI developer, data engineer or junior machine-learning role.
About the Role
A senior specialist position requiring advanced, independently demonstrated capability across the full data science and machine learning lifecycle:
- Statistical modelling, machine learning model development and analytics techniques
- Data-anomaly identification, data-quality governance and data-pipeline management
- Production MLOps - deployment, monitoring, drift detection and model adjustment
- Insight development and communication for C-suite, board-level and government stakeholders
- Strategic data science and AI input to organisational and client decision-making
- Applied Generative AI and LLM/RAG system design, evaluation and deployment
- Multi-regulatory data privacy and governance
- Cross-sector, multi-market APAC enterprise delivery
Key Responsibilities
Analytics & statistical modelling
- Design, develop, validate and deploy advanced analytical solutions combining statistical modelling (Bayesian optimisation, gradient-boosting, random forest, neural networks, LSTM, K-Means, uplift modelling, anomaly detection), machine learning frameworks (XGBoost, scikit-learn, TensorFlow, PyTorch) and database-integrated programming (Python, R, SQL, Spark) to solve enterprise data science problems across multiple client industries.
- Conduct exploratory data analysis, feature engineering, hypothesis testing, A/B and multivariate experimentation, and predictive and prescriptive modelling on complex, large-scale organisational datasets.
- Operate across cloud analytics platforms (Databricks, AWS, GCP, Azure); design and optimise data pipelines, delta lakes, feature stores and model-input datasets for production ML systems.
Data quality & anomaly resolution
- Identify, investigate and resolve data anomalies, quality issues, outliers, statistical deviations and analytical limitations in large-scale datasets sourced from heterogeneous systems - transactional databases, IoT/sensor telemetry, document repositories, digital event streams and external API feeds.
- Design and operate real-time drift-monitoring frameworks and statistical quality checks (MLflow, Databricks Delta Live Tables, custom anomaly-detection pipelines) to detect and resolve data and model-integrity issues in production.
- Govern data quality and enforce analytical standards across client datasets subject to multiple regulatory frameworks.
Insight development & stakeholder advisory
- Translate complex statistical, machine learning and AI model outputs into actionable insights, recommendations and decision-support tools calibrated for senior executives, boards, C-suite, government stakeholders and technical teams.
- Develop data visualisations, executive dashboards and analytical reports that make findings accessible to non-technical leaders.
- Lead workshops and technical discovery/advisory sessions to define analytical problems, scope data requirements, validate modelling approaches and agree success measures.
- Produce applied-AI research outputs, white papers and technical frameworks for clients and internal knowledge management.
Model monitoring & lifecycle governance
- Establish and maintain production MLOps pipelines (MLflow model tracking, Databricks Delta Live Tables, LangGraph orchestration, CI/CD) ensuring deployed ML and AI models are governed, reproducible and continuously evaluated against agreed benchmarks.
- Systematically review deployed models for performance drift, accuracy degradation, bias indicators and alignment with changing business and regulatory requirements; execute or direct retraining, recalibration, feature updates and architecture adjustments.
- Maintain model-governance documentation, regulatory-compliance records and audit-ready model performance histories consistent with responsible-AI obligations.
Strategy & innovation
- Partner with the Managing Director and senior client stakeholders to define AI and data science strategy, identify high-value ML and Generative-AI applications, prioritise analytical investment and develop AI roadmaps.
- Lead the design, architecture and delivery of innovative AI systems including production LLM/RAG applications, vision-language document-intelligence pipelines and cross-modal AI systems.
- Contribute to positioning, proposal development and technical advisory for new engagements; represent the organisation in industry, government and academic forums.
Additional responsibilities
- Design, build and maintain Databricks-based MLOps frameworks (Delta Live Tables, LangGraph agents, feature stores, MLflow registries) for enterprise clients.
- Lead and mentor distributed teams of data scientists, ML engineers and analytics consultants.
- Manage data privacy, model explainability, responsible AI, bias testing and analytical risk management across all client engagements.
Essential Skills and Experience
Given the simultaneous, multi-regulated, multi-sector nature of our active client programmes, the successful candidate must demonstrate all of the following:
Experience & depth
- Minimum 7 years of progressive, full-time experience in data science, machine learning, advanced analytics and applied AI, including hands-on model development, production deployment, model governance and executive advisory.
- Demonstrated experience designing, deploying and monitoring machine learning models in production - supervised and unsupervised learning, time-series forecasting, deep learning and ensemble methods.
- Demonstrated experience applying statistical, predictive and prescriptive analytics to complex enterprise datasets, and recognising and overcoming data anomalies in production environments.
- Proven track record of reviewing, monitoring and adjusting deployed models (drift detection, retraining, lifecycle governance).
Multi-sector enterprise delivery
- Demonstrated enterprise data science / AI delivery across at least three of the following sectors: banking/financial services, telecommunications, energy infrastructure, healthcare, insurance, retail or government.
- Demonstrated experience delivering production Generative AI / LLM / RAG systems in a regulated environment (e.g. healthcare, banking or government), including retrieval design, evaluation frameworks, reranking and guardrails.
Platform & technical
- Demonstrated advanced production experience with Databricks: Delta Live Tables, MLflow, LangGraph and feature stores.
- Demonstrated experience across at least two major cloud platforms (AWS, GCP, Azure).
- Strong hands-on Python, SQL and R, plus NumPy, Pandas, scikit-learn, TensorFlow and/or PyTorch.
Governance
- Demonstrated experience managing data privacy and governance under the Australian Privacy Principles (APPs) and at least one additional international data-privacy regime (e.g. GDPR or PDPA).
Stakeholder & communication
- Demonstrated experience advising C-suite, board-level and/or government stakeholders and converting analytical outputs into strategic decisions, with strong written/verbal communication for non-technical audiences.
Qualifications
- A bachelor degree or higher in data science, software engineering, computer science, information systems, artificial intelligence, business analytics, mathematics, statistics, engineering or a closely related technical discipline.
Highly Regarded Experience
- Experience delivering regulated-healthcare AI or clinical decision-support systems (e.g. LLM/RAG applications in primary care).
- Predictive-maintenance ML for energy infrastructure using IoT and time-series data.
- Advising or partnering with Databricks on enterprise MLOps delivery.
- APAC multi-market delivery experience across two or more countries in the region.
- A postgraduate qualification in data science, business analytics, artificial intelligence or a closely related field.
- Published applied AI/ML research or technical white papers in recognised repositories or peer-reviewed venues.
- Department or team leadership of data scientists and ML engineers.
- Responsible-AI, explainability, bias and fairness testing in client-facing regulated environments.
What We Offer
- A senior leadership position at the technical core of a growing AI consultancy.
- Exposure to high-impact, frontier AI work across multiple regulated industries and markets.
- Competitive remuneration (AUD 220,000 – 240,000 plus superannuation).
- For suitably qualified candidates, employer-sponsored visa arrangements may be available.
How to Apply
Submit a current CV and a cover letter addressing your experience in: machine learning model development and statistical/predictive/prescriptive analytics; data-anomaly identification and resolution; MLOps and model monitoring/adjustment; insight development and executive advisory; data science strategy and AI innovation; production GenAI/LLM/RAG; the industry sectors you have delivered in; and the data-privacy frameworks you have worked within.
Applications will be assessed on demonstrated experience, technical data science capability, multi-sector and multi-jurisdiction delivery evidence, production AI systems knowledge and communication skills.
Pay: $220,000.00 – $240,000.00 per year
Work Location: Hybrid remote in Barangaroo NSW 2000
