SENIOR DATA SCIENTIST

Stuart is a leading tech-enabled logistics platform that transforms on-demand delivery across sectors like food, grocery, and retail.
Operating in over 130 cities across Europe, Stuart connects businesses with a network of independent couriers, providing access to fast, flexible, and efficient deliveries.
Our Mission We are an impact-driven company that aims to build the future of logistics for a more sustainable world.
shared, efficient and reliable.
We are committed to creating a new standard for urban deliveries that meet today’s environmental and social challenges while offering a premium delivery experience blending speed, flexibility and convenience.
Stuart is a highly diverse and inclusive company of 200+ employees from different nationalities and backgrounds working across France , Poland , Spain and the UK.
It’s the right moment and the right place for us to make an impact on millions of people, as home delivery services hit a record high.
And guess what? You can help us fulfil our vision About the Role You will join our Data Science team and play a key role in driving algorithmic solutions that enhance Stuart’s delivery network and scale it to optimum performance.
This role goes beyond dashboards or reporting, you’ll tackle real-world optimization and machine learning product challenges that directly shape Stuart’s operations, from real-time dispatch to long-term delivery network optimization.
As a Senior Data Scientist, you'll lead the development of algorithmic and ML-based solutions, deliver insights, and identify high-leverage opportunities to improve efficiency across the organization.
You'll work on classical machine learning problems as well as operations research and optimization topics.
Key areas of focus include.
- Dispatcher Solver Optimization.
Refine algorithms for solving complex routing problems (e.
., VRP) with the right trade-off between speed and precision.
- Incentive Optimization.
Develop strategies to guide drivers to high-demand areas while optimizing cost-effectiveness.
- Ride Acceptance Prediction.
Model the likelihood of drivers accepting delivery tasks to improve task allocation.
- Estimated Times of Arrival (ETAs).
Build models to predict delivery times and risk signals across the package lifecycle.
- Experimentation at Scale.
Improve and scale Stuart’s A/B and Switchback experimentation capabilities to support data-informed decision-making and other challenges where you’ll have direct impact on Stuart’s mission.
This is a hands-on, high-impact role suited for someone with an analytical mindset, a strong product intuition, and a passion for turning models into ML products that deliver measurable value.
You won’t just build models, you’ll help bring them to life in production, working closely with ML Engineers to integrate them into real-time services and data platforms.
Hybrid working model.
3 days/week in the office (Central Barcelona) What will I be doing? Problem Discovery & Exploration - Drive scientific discussions around algorithms and modeling approaches, bringing deep analytical thinking and creative insights.
- Formulate hypotheses, design experiments to validate them, and analyze the data to guide decisions.
Solution Design & Prototyping - Build and iterate on proof-of-concept ML and optimization models grounded in real business needs.
- Propose algorithmic improvements to solver logic, heuristics, and objective functions to better meet business constraints.
- Develop predictive models for supply optimization, network patterns, personalized heatmaps for couriers, ETA & risk signals, and routing strategies.
Engineering & Productionization - Partner with ML Engineers, Platform Engineers, Analysts, and Product Managers to ship robust ML services.
- Contribute to data and feature pipelines, and ensure models are versioned, monitored, and reliably deployed.
- Set up monitoring and alerting protocols to track production performance and data quality.
Culture & Collaboration - Promote best practices in experimentation, model development, and data-driven thinking across teams.
- Help strengthen a culture of curiosity, rigor, and end-to-end ownership.