Production ML Scientist: Model Reliability & Growth Analytics

Mutinex; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Mutinex, an AI-powered growth platform, seeks a data science professional to ensure models are accurate, reliable and production-ready. You will apply modelling tech to new data, monitor production outputs and improve diagnostics behind model delivery.

This role spans data wrangling, feature engineering and methodology collaboration with Data Science and Engineering, delivering trusted results for customers and stakeholders.

Qualifications

• Strong analytical foundations and statistical thinking.
• Fluent in Python and SQL with ability to transform and validate data.
• Write maintainable, tested code and embrace code reviews.
• Experience with production ML systems and monitoring workflows.
• Ability to diagnose data issues, configurations and modeling limits.
• Comfort working across Data Science, Engineering and product teams.
• Experience with time-series modelling or marketing analytics is useful.

Responsibilities

• Deliver reliable customer models using Mutinex technology with high accuracy and timeliness.
• Monitor inputs, outputs, and performance in production to catch drift and quality risks.
• Diagnose issues by linking model behavior to data or configuration changes.
• Automate repeatable work with reusable data checks and tests.
• Improve observability with clear visualisations and alerts for model behavior.
• Collaborate with Data Science on methodology, feature engineering and validation.
• Partner with Engineering to improve data access and production reliability.
• Contribute to peer review, standards, and quality gates for the team.
• Communicate findings, uncertainty and trade-offs clearly to colleagues.

Skills

Analytical foundations

Python

SQL

Code quality

Systematic diagnostics

Production ML

Cross-disciplinary collaboration

Time-series analysis

Tools

GCP

Job description

Mutinex, an AI-powered growth platform, seeks a data science professional to ensure models are accurate, reliable and production-ready. You will apply modelling tech to new data, monitor production outputs and improve diagnostics behind model delivery.

This role spans data wrangling, feature engineering and methodology collaboration with Data Science and Engineering, delivering trusted results for customers and stakeholders.