Data Scientist - Inside IR35 - Hybrid

• 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. You'll join a small, high-calibre team solving complex logistics and optimisation challenges with meaningful real-world impact., 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

Essential

• 3-6+ 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)