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Greater London, England, United Kingdom
Onsite
Scarlet is authorised to assess and certify medical devices. We combine clinical, technical and regulatory expertise with AI agents and software so rigorous certification can keep pace with product development at the world’s most ambitious technology companies, without lowering the safety bar.
Our customers have cut a year or more from their certification timelines for new AI-enabled medical devices and shortened product-update cycles from months to weeks. You’ll join a team building the infrastructure that makes these outcomes repeatable at scale.
As Scarlet's first Deployment Optimisation hire, you will own how Scarlet plans, prioritises and deploys assessment resources across our customer portfolio. You will build the operating model for a fast-growing portfolio: in twelve months’ time, we expect to serve roughly four times today's customer base — assessing all types of devices, across a range of regulatory frameworks and standards.
You will solve a complex global scheduling problem. Our assessment experts are distributed around the world, many work part-time or as contractors, and some customer assessments must happen in person. You will build systems that match each assessment with the right expertise, location and timing.
You will combine operational judgement, data modelling and AI to build planning and deployment systems that keep every assessment moving, from demand forecasting to real-time prioritisation across the portfolio. Your work will determine how quickly safe medical devices reach the patients who need them.
Own portfolio-level capacity planning: forecast how much assessment resource, and of what type, we will need at a given time to meet demand, to ensure hiring and onboarding plans are aligned with capacity needs.
Design and optimise how assessment experts are allocated to work, including who performs each activity and when it can begin.
Own and improve the scheduling and prioritisation tools used to track evaluator availability, customer availability and qualification status across certification regimes.
Partner with product, engineering and machine learning teams to build tools that support automation of large-scale assessment operations.
Experience building or operating production forecasting, optimisation or scheduling systems.
Experience using data modelling and analytics to drive operational decisions, with strong hands-on skills.
Experience using AI to improve operational workflows, including evaluating when their outputs are reliable enough to use.
Experience leading cross-functional decisions involving teams with competing priorities.
Experience explaining complex operational decisions clearly.
Experience optimising complex scheduling, resource allocation or supply chains in a logistics-heavy service delivery business
Experience designing or operating systems subject to regulatory, certification or qualification constraints.
Experience working with AI agents for operational workflows.
