Location : Hybrid (London, UK) Contract Duration : Initial 612 months Rate : Competitive Why this role? Real-World Impact: Build models that directly influence live fleet operations Applied ML Focus: Time-series, geospatial data, optimisation problems Complex Systems: High-volume, real-time operational data Autonomy: End-to-end ownership from modelling to deployment About the Role We are recruiting on behalf of a mobility technology business building intelligent fleet orchestration systems. This role suits an experienced Applied Machine Learning Engineer or Data Scientist comfortable working with messy real-world data, operational constraints, and production systems. Youll join a small, high-calibre team solving complex logistics and optimisation challenges with meaningful real-world impact. Key Responsibilities Develop predictive models using time-series and geospatial datasets Design and iterate on demand forecasting models Support fleet positioning and operational planning initiatives Engineer features from large-scale operational datasets using Python and SQL Design and evaluate experiments tied to business KPIs Collaborate with engineering teams to deploy and improve models in production Participate in technical discussions, code reviews, and agile deliver...