Data Scientist (AI)

As a Data Scientist (AI) on Heidi’s Model Team, you’ll sit at the intersection of AI engineering and data science. In the short term, you’ll partner closely with our AI Engineering team to strengthen the foundations of our data pipelines, analytics, experimentation frameworks, and reporting systems.

Over time, this role will evolve into a hands-on contributor in AI/LLM model work—spanning fine-tuning, deployment, personalization, and applied research.

This is a unique opportunity for someone who is currently a Data Scientist in Australia or New Zealand, who feels limited by purely analytical challenges and wants to transition into a more technically demanding role that blends engineering rigor with cutting-edge AI innovation.

You’ll grow with the team, expanding from data-centric responsibilities into world-class applied AI science.

What you’ll do:

• Experimentation: Collaborate with engineers and product teams to design, implement, and analyze online A/B tests to measure product impact.
• Analytics & Reporting: Design dashboards, run analyses, and provide clear reporting to inform product and research decisions.
• Model Fine-Tuning: Gain hands-on experience with large language models by applying fine-tuning techniques (e.g., supervised fine-tuning, parameter-efficient methods) to improve model performance in healthcare-specific tasks.
• Model Deployment: Support the engineering team in deploying models into production environments, ensuring scalability, reliability, and integration with our clinical workflows.
• Model Personalisation: Explore approaches for adapting models to specific user needs, such as personalization, domain adaptation, and context-aware inference to enhance clinician productivity and patient care.
• Collaboration: Partner with data, engineering, product, and medical knowledge teams to align data and model work with Heidi’s mission in healthcare AI.
• Continuous Learning: Stay up-to-date with emerging AI and ML research, and grow your expertise from data-focused tasks to advanced model science.
  

What we will look for:

• A background as a Data Scientist (or similar role) with strong skills in Python, SQL, and modern data tooling.
• Demonstrated experience in data analysis, experimentation (A/B testing), and building dashboards or reporting systems.
• Solid programming and software engineering skills: ability to write clean, efficient, and maintainable code that can scale into production systems.
• Good understanding of large language models (LLMs) and transformer architectures—you know how they work under the hood and are motivated to deepen this knowledge further.
• An interest and motivation to deepen technical expertise in AI/ML—particularly in areas like model fine-tuning, deployment, and personalization.
• A solid foundation in statistics, probability, and data-driven decision-making.
• Strong problem-solving skills with the ability to move from vague questions to well-structured experiments and insights.
• Curiosity, adaptability, and a growth mindset: you’re eager to bridge the gap between data science and AI engineering.

Bonus:

• Experience with machine learning workflows (e.g., training or evaluating models, working with ML pipelines).
• Familiarity with deep learning frameworks (PyTorch or TensorFlow).
• Prior exposure to healthcare data or applications.