Job Purpose
As a Machine Learning Product Analyst, you will be the in-house engine behind our AI/ML roadmap. This is primarily an ML role — you will design, build, and validate predictive models that directly influence clinical decision-making. You will also be the sole in-house member of the product team; you will own the translation of model insights into shipped product features, working hand-in-hand with the engineering team to
bring your work to life.
This role is built for someone early in their career who is technically strong, curious about healthcare, and hungry to grow quickly. You will have direct access to leadership, real clinical data, and a problem space where your work has measurable impact on patient outcomes.
Key Responsibilities
Machine Learning & Data Science (Primary Focus)
- Explore, clean, and analyse structured clinical and operational datasets to identify patterns, signals,
and predictive features - Build, validate, and iterate on predictive ML models
- Own the full ML lifecycle: feature engineering, model selection, performance evaluation, deployment
readiness, and post-release monitoring - Work with our engineering partners to integrate model outputs into production software in a reliable,
explainable, and auditable way - Document model development and validation in line with Software as a Medical Device (SaMD)
standards, including FDA AI/ML guidance requirements - Set up feedback loops to detect model drift and flag when retraining is needed
Product Management (Supporting Focus) - Translate clinical insights and ML findings into clear product requirements and user stories for the
external engineering team - Maintain and prioritise the AI/ML feature backlog in collaboration with the Head of Product
- Support sprint ceremonies and delivery tracking with the external engineering team
- Engage with clinical stakeholders and end users to validate that model outputs are useful,
interpretable, and trusted in practice - Contribute to regulatory documentation for AI/ML components of the platform
Candidate Profile
2–3 years of hands-on experience in data science, ML engineering, or a closely related analytical role
- Solid Python skills and familiarity with core ML libraries — scikit-learn, XGBoost, and/or PyTorch or
TensorFlow
Machine Learning Product Analyst – Job Description Page 2 - Experience working with real-world, messy datasets — cleaning, exploring, and deriving meaningful
features - An ability to explain technical work clearly to non-technical audiences — clinicians, product
stakeholders, or leadership - Curiosity, rigour, and a bias toward learning: you ask good questions, read papers, and aren’t afraid to
iterate when a model underperforms