CTO (Chief Technology Officer)
We are looking for a hands-on Fractional CTO / Technical Product Leader to support an established digital-health company at an important stage of product and technology evolution.
The company has a mature digital product, an established customer base and several years of accumulated data. The business is now exploring how to make better use of its technology and data to support the next stage of product development.
The founder is looking for an experienced technical leader who can help assess the current product and technology landscape, identify the highest-value opportunities and translate them into a practical, achievable technical roadmap.
The longer-term vision is to evolve from a workflow/product platform towards:
structured clinical data → analytics → decision support → potentially intelligent recommendations for therapy programmes or interventions.
One potential opportunity being considered is an AI-powered scribe that could capture richer information from therapy sessions and turn it into structured data.
The key question is:
What should the company build next, given its existing product, data, engineering team and budget — and what technical foundations are needed to support its longer-term vision?
We are looking for someone who can combine strong technical depth, product thinking and pragmatic decision-making — particularly in an environment where development resources and budget need to be carefully prioritised.
Key Requirements:
- Hands-on Fractional CTO / Technical Product Leader, not just a strategic advisor.
- Proven experience building/scaling digital-health SaaS products, involving clinical workflows and patient/longitudinal data.
- Strong technical depth in backend, APIs, databases, cloud, and architecture.
- Strong health/clinical data architecture experience, especially longitudinal and patient-generated data.
- Practical AI/LLM experience: clinical scribes, transcription, structured extraction, RAG, and evaluation — but not a pure AI/ML researcher.
- Able to make build vs. buy vs. postpone decisions and translate product/clinical objectives into a realistic 3–6 month technical roadmap.
- Understanding of health-data privacy, consent, access control, auditability, and governance.
- Experience with startups/small engineering teams and constrained budgets.
- Comfortable working directly with a non-technical healthcare founder and staying close to developers.
- Available for fractional ongoing engagement, potentially starting around 1–2 days/month.
- Strong plus: has previously taken a healthcare product through workflow SaaS → proprietary clinical dataset → structured data → analytics → decision support.
- Previous exposure to clinical scribes, transcription, structured extraction, RAG, and AI evaluation.
- Experience assessing an existing product, architecture, dataset, and development team, rather than only building greenfield products.
Nice to Have
- Experience specifically with paediatrics, disability care, therapy, or allied health would strengthen the fit
- Strong product thinking alongside engineering: understanding which data should be collected and which capabilities are worth building now versus later.
- Experience obtaining value from a proprietary healthcare dataset, particularly moving toward analytics and decision-support capabilities.
- Comfortable operating with a bootstrapped/constrained-budget company and making pragmatic technology choices.
Key Responsibilities
- Assess the current product, architecture, data, and engineering team to identify technical gaps and priorities.
- Define a practical 3–6 month technology and development roadmap aligned with client’s budget and longer-term vision.
- Design the data architecture needed to turn existing and future clinical/therapy data into structured, usable longitudinal datasets.
- Determine what additional data the client should collect and how it should be captured, stored, governed, and made usable for analytics and AI.
- Evaluate whether the proposed AI scribe is the right next investment, including build vs. buy vs. postpone decisions.
- Guide the technical progression from workflow SaaS → structured data → analytics → clinical decision support / recommendations.
- Make concrete decisions around backend, APIs, databases, cloud infrastructure, and integrations.
- Guide practical implementation of AI/LLM capabilities, including transcription, structured extraction, RAG, and evaluation where appropriate.
- Ensure appropriate privacy, consent, access control, auditability, and health-data governance.
- Work directly with the founder and existing development team, translating clinical/business objectives into technical requirements and executable development work.