Company Description

Nine is Australia’s largest locally owned media company. Working at Nine, you’ll have access to a unique range of experiences and opportunities, helping drive the success of the country’s most trusted television, radio, digital and publishing brands.

Our content reaches almost every Australian - meaning what we do has real impact. We bring people together, celebrate the big moments, and capture the everyday ones.

Some of our most beloved brands have been part of Australian life for generations, and others - new on the scene - have already found their place firmly in our lives. We’re evolving and we need people like you to bring new ideas, innovate and make your mark.

Job Description

The data science team develops, builds and delivers data science products to provide insights and solutions to business problems. Key responsibilities include gathering and analyzing data, developing and implementing data science models, and communicating findings to stakeholders. We push the envelope in what is possible and work on new and novel implementations of data science techniques.

Areas of focus for the team include implementing generative AI to aid our content creation stakeholders, building recommendation and personalisation solutions and forecasting important metrics to drive data driven decision making.

Day to day you will:

Building and managing scalable machine learning pipelines for various stages from training to deployment.
Working with data scientists to move models from development to production environments.
Developing and managing CI/CD and continuous training workflows for ML models.
Implementing monitoring for production models and optimizing them for performance, scalability, and cost.
Working with data and platform engineers to ensure data quality and build efficient data pipelines for AI/ML.
Continuously learning and applying the latest industry best practices.
Championing best practices within the data science team, including code quality and testing.
Contributing to a collaborative culture through knowledge sharing and mentorship.
Adhering to data privacy regulations and ethical standards for all machine learning systems.

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