Data Scientist

Overview

Who are Heidi?

Heidi is building an AI Care Partner that supports clinicians every step of the way, from documentation to delivery of care.

We exist to double healthcare’s capacity while keeping care deeply human. In 18 months, Heidi has returned more than 18 million hours to clinicians and supported over 73 million patient visits. Today, more than two million patient visits each week are powered by Heidi across 116 countries and over 110 languages.

Founded by clinicians, Heidi brings together clinicians, engineers, designers, scientists, creatives, and mathematicians, working with a shared purpose: to strengthen the human connection at the heart of healthcare.

Backed by nearly $100 million in total funding, Heidi is expanding across the USA, UK, Canada, and Europe, partnering with major health systems including the NHS, Beth Israel Lahey Health, MaineGeneral, and Monash Health, among others.

We move quickly where it matters and stay grounded in what’s proven, shaping healthcare’s next era. Ready for the challenge?

The Role

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.

What we believe in

Heidi builds for the future of healthcare, not just the next quarter, and our goals are ambitious because the world’s health demands it. We believe in progress built through precision, pace, and ownership.

- Live Forever - Every release moves care forward: measured, safe, and built to last. Data guides us, but patients define the truth that matters.

- Practice Ownership - Decisions follow logic and proof, not hierarchy. Exceptional care demands exceptional standards in our work, our thinking, and our character.

- Small Cuts Heal Faster - Stability earns trust, speed delivers impact. Progress is about learning fast without breaking what people depend on.

- Make others better - Feedback is direct, kindness is constant, and excellence lifts everyone. Our success is measured by collective growth, not individual output.

Our mission is clear: expand the world’s capacity to care, and do it without losing the humanity that makes care worth delivering.

Why you will flourish with us

- Flexible hybrid working environment, with 3 days in the office.

- Additional paid day off for your birthday and wellness days.

- Special corporate rates at Anytime Fitness in Melbourne, Sydney tbc.

- A generous personal development budget of $500 per annum.

- Learn from some of the best engineers and creatives, joining a diverse team.

- Become an owner, with shares (equity) in the company, if Heidi wins, we all win.

- The rare chance to create a global impact as you immerse yourself in one of Australia’s leading healthtech startups.

- If you have an impact quickly, the opportunity to fast track your startup career

Help us reimagine primary care and change the face of healthcare in Australia and then around the world.

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