Data Scientist – Mobility & Operations Intelligence (Contract)
London (Croydon) (Hybrid – typically 3 days per week in office)
6–7 Month Contract (Strong likelihood of full-time conversion)
Approx. £55,000 annualised equivalent (depending on experience)
About Odysse
Odysse is a London-based mobility technology company building intelligent fleet orchestration systems for ride-hailing and future autonomous vehicle (AV) networks.
Our AI-driven decision systems influence real-world behaviour: Where vehicles move, how cities are served, and how efficiently transportation operates. We are designing the optimisation and data infrastructure that supports both today’s human-driven fleets and tomorrow’s autonomous mobility networks.
This is a hands-on applied machine learning role focused on building and improving decision systems that directly influence live fleet operations and contribute to long-term autonomous fleet orchestration capabilities.
You will work on logistics optimisation, real-time decision systems, simulation and operational experimentation, applying ML in complex, real-world environments.
What You’ll Work On
- Build predictive models using geospatial and time-series data (demand, driver behaviour, trip outcomes) and evaluate them using operational business metrics
- Partner with operations and senior team members to translate operational challenges into measurable ML problems and propose appropriate modelling approaches
- Engineer features, analyse large datasets using Python and SQL, and identify useful external data sources
- Design and support experiments contributing to fleet positioning and planning decisions
- Contribute to modelling and simulation work that supports long-term autonomous fleet orchestration and mixed-fleet (human driven + Autonomous Vehicle) operational planning
- Collaborate with operations and engineering to deploy and improve data-driven workflows
We’re Looking For Someone Who
- Ideally has 3-5 years’ experience in Data Science / Applied ML / Analytics (years of experience provided as a guide)
- Can independently train, evaluate and iterate on models given a clearly defined problem
- Is comfortable with Python (pandas/numpy/sklearn or similar), strong SQL, and relational databases
- Can work with imperfect real-world data and optimise for practical impact rather than just model accuracy
- Has exposure to advanced modelling approaches (e.g. neural networks, optimisation, or reinforcement learning)
Nice to Have
- Experience with time-series or geospatial datasets, experimentation or optimisation problems
- Experience in logistics, marketplaces, mobility systems, ride-hailing or autonomous vehicle ecosystems
Why This Role Is Different
- Your models affect physical movement in a city, not just clicks on a screen
- Exposure to real operational decision systems used in live fleet environments
- Opportunity to help build the data and optimisation foundations for future autonomous vehicle networks
- Work across modelling, experimentation and deployment in a product environment shaping next-generation mobility
- Work closely with senior leadership team, with exposure to global corporate partners, interacting with venture capital and strategic funders, on ambitious projects shaping the future of mobility
