Top Machine Learning Jobs in New Zealand

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Annotate and label text, objects, and visual data for AI and machine learning projects. Review datasets for accuracy, follow detailed project standards, and maintain data quality. The role includes full onboarding and training, requires attention to detail and analytical reasoning, and can be performed remotely on a flexible full-time or part-time schedule.

Lead end-to-end development of predictive machine learning systems for agricultural operations, including modeling, dataset construction, training, deployment, monitoring, and production support. Conduct research across machine learning and pasture science, work with complex high-volume datasets, and translate academic research into practical systems. Collaborate across engineering, product, and design while developing innovative models for pasture growth, animal health, remote sensing, and grazing optimization.

Shapes Watercare’s data, analytics and AI architecture by developing roadmaps, standards, patterns and high-level designs. Provides architectural guidance across BI, data warehousing, ETL, machine learning and emerging AI technologies, while assuring project and programme solutions. Works with technical and business stakeholders to create scalable solutions, influence technology decisions, and mentor architects and technical specialists.

The Senior Data Scientist will collect and transform complex datasets, develop and deploy machine learning, NLP, generative AI, and agent solutions, and optimize models for accuracy, explainability, and ethical alignment. Responsibilities include building custom transformer models, monitoring production pipelines, communicating insights through visualizations and presentations, and collaborating with cross-functional teams to drive productivity, revenue, and strategic decision-making.

Provides advanced credit risk analysis for CECL and stress test loss forecasting across commercial and consumer lending portfolios. Responsibilities include portfolio analysis, variance investigation, risk dashboards, model documentation, audit and validation support, and senior management reporting. The role also evaluates statistical forecasting methods, AI and machine learning applications, and process automation while partnering with credit risk, finance, and cross-functional stakeholders.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Build and improve core AI and machine learning infrastructure enabling teams to develop, train, evaluate, deploy, and operate ML models and pipelines. Design scalable distributed systems, support end-to-end ML lifecycles, implement MLOps automation, collaborate with product engineering teams, lead projects from design through launch, review code, document solutions, and mentor junior engineers. Work may include dataset curation, LLM fine-tuning, model serving, monitoring, and generative AI systems.

Lead the technical direction of Watercare’s enterprise data and analytics capability. Establish standards and reference patterns, design and assure complex solutions, review code and releases, resolve platform and pipeline issues, and contribute hands-on to data products, models, dashboards, and pipelines. Coach multidisciplinary teams, support governance and scalability, and communicate technical trade-offs to technical and non-technical stakeholders.

Develop machine learning and modeling-and-simulation solutions for complex manufacturing systems. Build digital twins, surrogate models, and optimization tools using engineering principles, physical data, advanced analytics, and ML. Analyze large datasets, support virtual manufacturing qualification, optimize production and warehouse processes, evaluate build-versus-buy solutions, and translate technical insights into actionable business recommendations while collaborating with R&D scientists, engineers, and technology partners.

Leads end-to-end data science and AI/ML initiatives, including problem formulation, experimentation, model deployment, evaluation, optimization, and production monitoring. Develops prescriptive AI solutions, foundation-model and agentic-system evaluation frameworks, responsible-AI controls, RAG and knowledge-graph integrations, and model telemetry. Partners cross-functionally to improve business metrics, translate technical outputs into value, and mentor junior data scientists.

Design and develop end-to-end IoT solutions connecting devices, embedded systems, data platforms, cloud services, and APIs. Responsibilities include implementing communication protocols, building secure architectures, developing data pipelines, testing and troubleshooting systems, improving reliability, evaluating emerging technologies, and collaborating with hardware, software, engineering, and product teams.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

Develop and design software across ERP, APIs, integrations, mobile applications, and web solutions. Lead technical implementation decisions, mentor developers, contribute to architecture, refine backlogs, identify risks and dependencies, support sprint planning, and promote secure, maintainable engineering practices. The role also contributes to cloud deployment, DevOps improvements, technical product decisions, and adoption of AI-assisted development tools.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Manages ML Ops engineers and leads development, deployment, and operation of production machine learning systems. Owns ML engineering and data science tooling roadmaps, scalable cloud infrastructure, ETL pipelines, model serving, monitoring, testing, and CI/CD practices. Provides technical leadership to data science managers, coaches engineers, establishes best practices, and drives reliable ML lifecycle implementation using Google Cloud technologies and distributed computing platforms.

Provide agricultural science expertise to shape predictive grazing systems: translate biology into testable hypotheses, design and run experiments, validate and interpret models, define measurement protocols, identify new datasets and sensors, and partner with ML engineers, product and data teams to improve model performance and product value for farmers.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Own end-to-end development of advanced ML systems for agricultural applications: research, model design, dataset construction, training, deployment, and production monitoring. Collaborate across engineering, product, and design to translate cutting-edge research into practical predictive products for pasture and animal management.

Lead pre-sales engagements for Provenir's decisioning platform: conduct customer discovery, design solution architectures, build tailored demos, respond to RFPs, collaborate with Sales, Data Science, and Professional Services, and serve as subject matter expert in credit risk, fraud, and financial crime to drive deals and ensure smooth delivery handoffs.

Provide technology and digital legal advice on procurement, outsourcing, IP, privacy/data protection, cybersecurity incident response, AI/biometric/blockchain projects, commercial negotiations, M&A of tech assets, and IT disputes for complex, cross-border transactions.

Design, develop, and deploy custom ML models and transformer-based NLP systems. Build and maintain ML pipelines, fine-tune LLMs, implement RAG, monitor production performance, research new techniques, and mentor junior engineers.