Data Scientist

Randstad; Auckland; On-site; NZD 90,000 - 150,000; Full time

Job summary

WorkSafe New Zealand is seeking Data Scientist to apply advanced analytics, ML and AI to regulatory and organisational problems. You will develop models and AI products to inform decision‑making, improve risk understanding and support safer workplaces across New Zealand.

You'll work with data engineers, analysts and business stakeholders to deliver end‑to‑end solutions and translate complex findings into practical insights for frontline teams and leadership.

Qualifications

• Experience with statistical, ML and AI techniques.
• Ability to work with data engineers and analysts.
• Strong communication of analytical findings.

Responsibilities

• Design, build and validate advanced analytical models and AI solutions.
• Apply statistical, machine learning and AI techniques to complex problems and datasets.
• Design evaluation approaches for model and AI outputs.
• Support transition of models and AI products into operational use and monitor performance.
• Partner with data engineers, analysts and stakeholders to deliver end‑to‑end solutions.
• Translate analytical findings into practical insights for decision-makers.

Job description

Jora New Zealand will close on 9th September 2026. Thank you for being with us, we are cheering you on as you continue your career journey.

2x Fixed-term positions until 30 June 2027 available - can be based at any WorkSafe office location

We are open to inter-agency secondments, please speak to your manager before applying

WorkSafe New Zealand | Mahi Haumaru Aotearoa Is The Primary Workplace Health And Safety Regulator. Our Vision Is That Everyone Who Goes To Mahi Comes Home Healthy And Safe. We Live By Our Values

• Whakakotahi – we are united in a strong purpose
• Tiakina mai – we are entrusted with a duty of care

Enterprise Planning and Implementation is a new group established at WorkSafe to strengthen planning, coordination, and implementation across the organisation, particularly to improve how work is prioritised, sequenced, and supported through to delivery.

The Data, Analytics and AI team is at the heart of enabling data-driven decision‑making across WorkSafe and is responsible for building and maintaining the data assets to meet WorkSafe’s data, insights and intelligence needs. Working closely with analysts, architects, engineers, and business stakeholders, the team develops the data foundations that support reporting, analytics, and organisational performance.

About Your Team / Ko Te Tīma

Enterprise Planning and Implementation is a new group established at WorkSafe to strengthen planning, coordination, and implementation across the organisation, particularly to improve how work is prioritised, sequenced, and supported through to delivery.

The Data, Analytics and AI team is at the heart of enabling data-driven decision‑making across WorkSafe and is responsible for building and maintaining the data assets to meet WorkSafe’s data, insights and intelligence needs. Working closely with analysts, architects, engineers, and business stakeholders, the team develops the data foundations that support reporting, analytics, and organisational performance.

About The Role / Ō Te Tūranga

As a Data Scientist, you'll play a hands‑on role in applying advanced analytics, machine learning and AI to complex regulatory and organisational problems. You'll work on things like risk‑targeting projects that support prioritisation of regulatory activity, and AI agents that support frontline and regulatory work. Working alongside data engineers, analysts and business stakeholders, you'll design and deliver models and AI products that inform decision‑making, improve our understanding of risk and harm, and support better outcomes for New Zealand workplaces.

What You’ll Do/ Ko tōu ake mahi

• Design, build and validate advanced analytical models and AI solutions
• Apply statistical, machine learning and AI techniques to complex problems and datasets, including LLM‑based approaches such as retrieval-augmented generation, agents and structured extraction.
• Design evaluation approaches for model and AI outputs, including test sets, human review and appropriate performance measures
• Support the transition of models and AI products into operational use, and monitor their performance once deployed
• Partner with data engineers, analysts and other specialists to deliver end‑to‑end solutions
• Translate analytical findings into practical insights, and clearly communicate what models can and cannot tell decision‑makers

What You’ll Bring / Ko ngā pukenga ōu