Data Scientist

• Meet patients and clinicians and deeply understand their unique needs and challenges
• Ideate, test, and refine prototypes and solutions to meet their needs and challenges
• Work on complex and unsolved problems
• Analyse data, generate insights and produce effective visualizations
• Collaborate with domain experts to generate use-cases for AI/ML solutions
• Develop and continuously perfect AI/ML solutions that improve healthcare outcomes
• Collaborate with the team to develop and improve processes for handling data at scale
• Deliver formal and informal presentations
• Continuously learn and maintain up-to-date knowledge of new developments in the field

• Owning the end-to-end delivery of data science solutions, from problem framing through to production and ongoing improvement
• Building advanced models, generative AI tools, and insight-driven products that go beyond what standard tools can achieve
• Partnering closely with stakeholders to test ideas, demonstrate value early, and iterate based on real-world feedback
• Working within agile squads and across domains, learning the business deeply and applying your expertise where it matters most
• Contributing to a growing data science capability, sharing knowledge, lifting standards, and helping shape how we work

• Turn potential and possibility into valid opportunity for Mosaiq’s users, through our data strategy and capability
• Getting hands-on with large, diverse SaaS datasets
• Exploring data to understand what signal exists and what doesn’t
• Designing experiments to test whether ideas are worth pursuing
• Validating assumptions early and kill bad ideas quickly
• Building machine learning models yourself, including feature engineering, model selection, training, validation, and evaluation
• Evaluating and employing the best technique for the job at hand
• Understanding model behaviour, failure modes, and limitations
• Creating fast, rough prototypes to prove or disprove value
• Showing what’s possible with data before engineering commits to building it
• Working closely with engineers to harden successful experiments
• Knowing when a prototype has done its job and move on
• Operate in ambiguity with incomplete data and shifting priorities
• Balance speed with rigour
• Make pragmatic trade-offs and explain them clearly
• Take ownership of problems rather than waiting for perfect information or conditions
• Explaining complexity in plain language
• Prioritise and show the evidence over the opinions
• Help the business understand what data science can — and can’t — do
• Advocate for a data-led approach in all aspects of our work

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Data Scientist

Data Scientist

Data Scientist