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 delivery Required Skills & Experience Essential 36 years commercial experience in Applied ML or Data Science Strong Python (pandas, numpy, sklearn or similar) Strong SQL Experience building and iterating on predictive models Conditional (must meet at least 2 of the below) Time-series modelling 2 years Geospatial data experience (H3, GeoPandas, PostGIS or similar) Optimisation / operations research exposure Logistics / mobility / marketplace domain experience Nice to Have Reinforcement learning Simulation modelling Experience deploying models into cloud environments Experimentation frameworks (A/B testing, model validation at scale) How to Apply If youre an Applied ML Contractor looking a new exciting opportunity working focused on real operational decision systems, get in touch. Please apply if interested and well aim to respond within 24 hours.