Top Data Science Jobs in New Zealand

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Build and maintain evaluation infrastructure for LLM-powered features, including datasets, ground-truth controls, automated pipelines, error analysis, labeling systems, and prompt optimization. Diagnose gaps between offline evaluations and production accuracy, scale tooling across AI products, and assess the impact of ontology and schema changes. The role requires strong Python, SQL, data science, LLM evaluation, and visual reasoning expertise, with collaboration across engineering, product, and AI teams.

Designs and delivers technical demonstrations, proof-of-concepts, architectures, prototypes, and custom applications for life sciences customers. The role addresses drug discovery, clinical analytics, regulatory workflows, omics processing, governance, reproducibility, and validation requirements. It partners with account executives, Customer Success, and Solutions Architects to convert evaluations into production deployments while building reusable technical assets and identifying new customer use cases.

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.

Work with Applied ML and DataQA on messy, real-world parts and vehicle data. Use SQL and Python to quantify data-quality issues, measure failure rates, decompose problems, produce reproducible analyses, and build lightweight tools or dashboards to inform Product and ML priorities. Paired with mentors, deliver actionable findings that improve parts validation, ordering, automatching, and variant handling.