Data Scientist

12-month contract

Negotiable on day rate, open to Pty Ltd

Azure Machine Learning (Azure ML) and MLOps best practices.

Key Responsibilities:

Understand business needs & value creation.

- Collaborate with stakeholders to identify business challenges where AI/ML can drive measurable improvements.
- Define success metrics and ensure alignment with business objectives.
- Rapidly build and validate ML prototypes to demonstrate feasibility and value.
- Iterate based on feedback, refining models and methodologies for production readiness.

Build and deploy scalable ML solutions

- Design, develop, and deployend-to-end AI/ML models onAzure MLto solve complex commercial challenges
- Own the entire ML lifecycle, includingdata preprocessing, feature engineering, model selection, hyperparameter tuning, model validation, deployment, and monitoring.
- Optimisefeature extraction techniques,



implementrobust model evaluationstrategies, and ensureeffective inference for real-time and batch scoringusingAzure ML Endpoints.
- Develop productive and reusable ML workflows and pipelines, ensuringautomated feature engineering, model retraining, and performance trackingacross multiple projects.
- Implement and enhance MLOps strategies, integratingCI/CD, automated monitoring, drift detection, and model retraining mechanismsto maintain model integrity over time.
- Collaborate with data engineers and software engineersto integratefeature stores, scalable data processing pipelines, and model-serving frameworksinto production applications, ensuring seamless deployment and reliability.

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📌 Data Scientist (Melbourne)
🏢 Mode Talent
📍 Melbourne