Data Scientist

Snapper

Data Scientist turning vast SaaS datasets into trustworthy insights for Mosaiq. Collaborate with engineers to build and evaluate machine learning models effectively.

Posted 5/9/2026full-timeWellington • 🇳🇿 New ZealandMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSCloudPythonSQL

About the role

Key responsibilities & impact

Turn potential and possibility into valid opportunity for Mosaiq’s users, through our data strategy and capability
Getting hands-on with large, diverse SaaS datasets
Exploring data to understand what signal exists and what doesn’t
Designing experiments to test whether ideas are worth pursuing
Validating assumptions early and kill bad ideas quickly
Building machine learning models yourself, including feature engineering, model selection, training, validation, and evaluation
Evaluating and employing the best technique for the job at hand
Understanding model behaviour, failure modes, and limitations
Creating fast, rough prototypes to prove or disprove value
Showing what’s possible with data before engineering commits to building it
Working closely with engineers to harden successful experiments
Knowing when a prototype has done its job and move on
Operate in ambiguity with incomplete data and shifting priorities
Balance speed with rigour
Make pragmatic trade-offs and explain them clearly
Take ownership of problems rather than waiting for perfect information or conditions
Explaining complexity in plain language
Prioritise and show the evidence over the opinions
Help the business understand what data science can — and can’t — do
Advocate for a data-led approach in all aspects of our work

Requirements

What you’ll need

At least 4 years of experience working as a Data Scientist in a SaaS or product-led environment
A qualification or comparable expertise in data science and analytics
Proven leadership and delivery experience on data science projects, particularly knowledge and capability in improving and measuring data science projects
Solid grounding in statistics and machine learning fundamentals
Experience in machine-learning operations, supporting an ability to deploy models into a production environment
Experience and confidence building models yourself, beyond the simple application of APIs
Capability in Python, AWS or similar cloud platform, SQL, working with notebooks (e.g. jupyter) and git
Experience with regression, time series analysis, optimisation, clustering and working with real, imperfect data
Confidence and comfort in building and owning outcomes, not just analysis
A team-centric, practical approach to problem solving

Benefits

Comp & perks

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Hard Skills & Tools

data sciencemachine learningfeature engineeringmodel selectionmodel trainingmodel validationmodel evaluationstatisticsregressiontime series analysis

Soft Skills

leadershipproblem solvingcommunicationownershippragmatismadaptabilitycollaborationprioritizationcritical thinkingdata advocacy

Certifications

data science qualificationanalytics qualification