Data Scientist

Mutinex; City of Melbourne; On-site; AUD 120,000 - 170,000; Full time

Job summary

Mutinex in Melbourne, Australia is seeking a data science professional to work within Model Scale. You will apply modelling technology to customer data, monitor production models, investigate unexpected behaviour, and improve automated tests and diagnostics behind model delivery.

You’ll collaborate across Data Science, Engineering and customer-facing teams to ensure reliable, production-ready outputs and scalable analytics in a probabilistic environment.

Qualifications

• Experience in data science, analytics or ML operations with real-world data.
• Fluency in Python and SQL.
• Ability to translate analytical work into production-quality tooling.

Responsibilities

• Deliver reliable customer models using Mutinex’s modelling technology on new data.
• Monitor model inputs, outputs and performance in production and identify anomalies.
• Diagnose issues by linking model behaviour to data or configuration changes; decide on fixes or escalation.
• Automate repeatable work with reusable data checks and diagnostics to speed delivery.
• Improve observability with clear visualisations, dashboards, monitoring and alerts.
• Collaborate with Data Science on feature engineering and methodology improvements from customer deployments.
• Partner with Engineering to improve data access and production reliability of systems.
• Contribute to peer reviews, documentation and shared standards to ensure quality and knowledge sharing.

Skills

Python

SQL

Statistical analysis

Time-series modelling

Tools

GCP

Job description

About Mutinex

Mutinex is an AI-powered growth platform used by 100+ global brands. Our measurement technology has processed over $9B in media spend and consistently outperforms legacy approaches.

We’ve spent years solving one of marketing’s hardest problems: knowing what actually works. Our product, GrowthOS, combines advanced data science with intuitive software to help marketers understand the impact of past decisions and make better choices about future spend.

The role

You’ll sit within Model Scale and help ensure our models produce accurate, reliable and decision-useful outputs for every customer. You’ll apply our modelling technology to new and refreshed datasets, monitor models in production, investigate unexpected behaviour, and improve the automated tests and diagnostics behind model delivery.

This is not a role where analysis ends with a notebook. You’ll work across the full path from messy customer data to production model outputs—partnering with Data Science on methodology and feature engineering, Engineering on platform reliability and automation, and customer-facing teams on the context behind the data.

The role suits someone who enjoys combining analytical depth with practical problem-solving. You’ll need the statistical judgement to evaluate model behaviour, the technical skills to investigate data and automate repeatable work, and the operational discipline to deliver consistently in a probabilistic environment.

What You’ll Do

• Deliver reliable customer models. Apply Mutinex’s modelling technology to new and refreshed customer data, maintaining a high bar for accuracy, consistency and timeliness.
• Monitor model quality. Evaluate model inputs, outputs and performance in production, identifying anomalies, drift and emerging quality risks before they affect customers.
• Diagnose issues systematically. Connect unexpected model behaviour to changes in data, configuration, implementation or methodology—and use sound judgement about when to fix, collaborate or elevate.
• Automate repeatable work. Build reusable data checks, model tests, exploratory workflows and diagnostics that make delivery faster and more reliable.
• Improve observability. Develop clear visualisations, monitoring and alerts that make model behaviour easier to understand and investigate.
• Partner with Data Science. Contribute to validation, feature engineering, feature extraction and methodology improvements based on what you learn from real customer deployments.
• Partner with Engineering. Improve data access, workflow automation, monitoring and the production reliability of the systems our models depend on.
• Strengthen how the team works. Contribute to peer review, documentation, shared standards and quality gates so knowledge and quality do not depend on one person.
• Communicate clearly. Make findings, uncertainty, risks and trade-offs understandable to technical and non-technical colleagues.

What We’re Looking For

You’ve worked in data science, applied statistics, analytics, ML operations or a related role and are comfortable working with real-world data and recurring analytical workflows. You can move between detailed investigation and the wider customer or business context, and you care about making sophisticated analysis reliable in practice.

Beyond Experience, We Care About How You Operate

• You have strong analytical foundations. You understand statistical uncertainty, hypothesis testing, Bayesian reasoning and common machine-learning approaches, and you know how to challenge your own conclusions.
• You’re fluent in Python and SQL. You can explore, transform and validate complex datasets and turn recurring work into maintainable tooling.
• You write code like an engineer. Your analysis doesn’t end in a notebook: you version-control your work, write tests, take review seriously, and produce code that colleagues can maintain and build on.
• You’re rigorous about quality. You validate results honestly, pay attention to detail and treat peer review and testing as essential rather than optional.
• You’re a systematic diagnostician. You can isolate the source of an issue and distinguish between a data problem, configuration error, engineering failure or modelling limitation.
• You’re comfortable with messy data. You can find structure, anomalies and useful signals in incomplete or inconsistent customer datasets.
• You understand production ML. You appreciate that a model is only valuable when it can run reliably, be monitored, recover from failure and produce outputs people can trust.
• You improve the system, not just the instance. You turn repeated manual work and recurring problems into reusable checks, documentation, automation or better process.
• You communicate with clarity. You can explain technical findings, uncertainty and trade-offs without hiding behind jargon.
• You work well across disciplines. You collaborate effectively with Data Science, Engineering, Product and customer-facing teams, bringing in the right expertise at the right time.
• You’re curious about the customer’s world. You work to understand different business domains and connect model outputs to the decisions they are meant to support.
• You take ownership. You raise risks early, follow problems through and contribute to the team’s shared knowledge and standards.
• You use AI as a power tool. You use it thoughtfully to accelerate exploration, analysis, documentation and automation while maintaining a high bar for validation.

Experience with time-series modelling, causal inference, marketing analytics, cloud platforms such as GCP, dashboarding tools, or production ML systems is valuable but not essential.

About The Team

Data Science is central to Mutinex’s product and customer value. Model Scale is where our modelling technology meets real customer data: applying models, maintaining quality, diagnosing problems and feeding what we learn back into our methodology and platform.

You’ll work directly with Data Scientists, ML and software Engineers, and customer-facing teams. The boundaries between these disciplines matter, so we expect people to collaborate closely, make ownership explicit and solve problems together rather than pass them between teams.

The bigger picture

This isn’t a role where you run the same analysis repeatedly and hand over the result. We’re building the systems, standards and tooling that allow advanced marketing measurement to be delivered reliably across many customers and datasets.

You’ll help make our models easier to monitor, diagnose and scale—and help turn the lessons from customer delivery into better methodology, infrastructure and product experiences. As your capability grows, there is room to move deeper into feature engineering, model development or ML engineering.

We care more about how you think, how you investigate and how you improve the work around you than whether you’ve followed one conventional career path. If you’re excited by hard data problems and want to help turn modelling into trusted customer outcomes, we should talk.