ABOUT US

Mutinex is a data and marketing analytics startup transforming the multi-trillion-dollar advertising industry by challenging the traditional consulting-based approach to Market Mix Modelling.
We are expanding our team and welcoming experienced talent to build collaboratively our globally-used and transformative marketing measurement product - GrowthOS.
We empower marketers by helping them to understand the value of their past decisions and the impact of future marketing spend.
With offices in both Melbourne and Sydney, we encourage you to work in the way that maximises your performance. Please note - this role can be based from either Sydney or Melbourne.

We are looking for a Senior/Staff Data Scientist to help grow our uniquely positioned Bayesian model stack. With a special focus on model development and marketing science, you will take charge of our models in finding ways to improve robustness and streamlining our parameter space. You will also support and lead the deployment of our model for new customers, working with enablement teams and domain experts to improve explainability.
In the long term, you will help drive automation improvements by collaborating with engineers to improve reuse and deployment of features/data signals. As a fast growing startup, your work will help refine our data platform by refining its engine: our state of the art marketing investment model. Improving our model and library of features will directly contribute to our growth.
The mission of our data science squad is to be the central nervous system of Mutinex.

Day to day

• Become an expert in our models, improving performance across new verticals/complex datasets.
• Rapidly iterate and test different model formulations on accuracy and causal integrity.
• Create new model outputs to help generate insight, owning features that will be used by world-class companies to make key business decisions.
• Develop automated feature extraction methods to create useful signals
• Excellent Python skills, we are all about production ready data science.
• Writing: improving our documentation for both data science and non-technical stakeholders; provide data science lens on insights and outputs from the model.