Snapper

Customer Success Manager

• The Customer Success Manager ensures customers and partners get real value from Mosaiq, Snapper’s transit intelligence platform
• Focuses on customer satisfaction, retention, and long-term success
• Works closely with the Head of Customer Success to create a consistent, high-quality experience
• Supports customers from onboarding through the full lifecycle
• Builds strong, trusted relationships and maintains engagement
• Identifies customer needs, insights, and feedback and shares internally
• Monitors customer health and engagement and supports renewals

Business Development Rep

• Identify, develop, and convert new business opportunities
• Own the full sales lifecycle from early engagement through to close
• Build and manage a strong, qualified pipeline within the transport and mobility sector
• Engage senior stakeholders across transport authorities, operators, and government agencies
• Shape solutions in collaboration with Product, Delivery, and Customer Success teams
• Manage complex, multi-stakeholder sales cycles and procurement processes
• Lead commercial negotiations and support contract execution
• Maintain disciplined pipeline management using CRM systems
• Represent Snapper at industry events, conferences, and customer meetings across NZ and AU
• Provide market insights to inform product and commercial decisions
• Support go-to-market planning through customer and industry feedback
• Focus on long-term customer value and successful outcomes

Business Development Manager

Data Scientist

• 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