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# Data Scientist

- **Company:** Allianz
- **Location:** Sydney, FL, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Online Services, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, SQL Databases, Feature Engineering, Large Language Models, Prompt Engineering, Generative AI, Agentic-AI, AI Platforms, Information Technology, Databricks
- **Published:** November 13, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pyvx6m5mxr

## About the Role

* Proven experience as a Data Scientist, solving complex business problems with data science and AI in a commercial setting.
 * You're passionate about using AI to change how a business works, not just building models. You want your work to make a measurable difference to customers and to the organisation.
 * Hands-on experience with Generative AI, LLMs and agentic frameworks, including prompt design, building agents and evaluating their outputs.
 * Strong Python and SQL, used across data engineering, machine learning, generative AI and model monitoring.
 * Experience with Microsoft ecosystem including Azure AI Foundry and related AI services would be preferred.
 * Experience working with Databricks (preferred)
 * Degree in a highly quantitative field such as Computer Science, Mathematics, or Statistics (or equivalent).
 * You're a strong problem-solver who handles ambiguity well and delivers practical solutions that make commercial sense.
 * You can explain complex ideas clearly to technical and non-technical audiences, and bring stakeholders along on the change.

## Description

The primary purpose of this role is to lead the way in utilizing Al techniques to enable AAL to deliver better customer outcomes, automate processes and make optimal business decisions which deliver real organisational value.

 * Work with business stakeholders to understand the problem behind each request, define what success looks like and decide whether AI is the right answer at all.
 * Apply human judgement to determine the best AI solution (rules, statistical model, LLMs or agentic) that best balances accuracy, explainability, cost and risk, not just the newest one.
 * Design and develop AI solutions end-to-end, from data exploration and feature engineering through to modelling, prompt design and agent orchestration.
 * Put models into production using systematic testing, quality assurance and ongoing monitoring for performance, drift and integrity.
 * Understanding AI risk and developing and documenting solutions with the right controls and guardrails in place to manage risk.
 * Track the business value of each solution after launch, and keep improving or retire models based on real-world results.
 * Turning model results into clear recommendations that executives can act on.
 * Coach and educate others in the team and across the business on data science methods, good practice and the value of AI.
 * Collaborating across teams to ensure solutions are robust, scalable, and sustainable.