Data Scientist (Fleet Operations)

TL;DR

Data Scientist (Fleet Operations): Optimising fleet operations through data-driven insights and operational research with an accent on operational research techniques, experimental methods, and causal inference. Focus on building frameworks for complex operational data, designing real-world experiments, and deriving actionable insights to improve on-road testing efficiency and decision-making.

Company

hirify.global develops Embodied AI technology for automated driving, enabling vehicles to perceive, understand, and navigate complex environments.

What you will do

• Develop frameworks to synthesize operational data (vehicle performance, route optimisation, and experiment scheduling) to inform strategy at product and company level.
• Define and refine fleet operations performance metrics aligned with business goals.
• Design and apply experimental methodologies to improve signal-to-noise ratio and speed up feedback loops for operational decision-making.
• Combine experimental methods with causal inference to test and optimise operational strategies.

Requirements

• 3+ years of experience in a Data Science role focused on operations research, process automation and optimisation, or similar.
• Proficiency in querying and building large datasets, including writing production-level SQL for data transformation pipelines.
• Experience designing and evaluating real-world experiments (e.g., A/B testing) to optimise operations and performance.
• Solid understanding of statistical principles, including hypothesis testing, distributions, and method assumptions.
• Proficiency in a statistical scripting language (Python or R) and relevant packages (e.g., pandas, sklearn, statsmodels).
• Ability to summarise, visualise, and communicate data insights clearly, with a track record of influencing operational improvements.

Culture & Benefits

• Full-time role based in the London office with a hybrid working policy (office/workshops plus time working from home).
• Focus on innovation, culture, relationships, and learning through time together in offices and workshops.
• Inclusive interview experience and commitment to diversity, fairness, and respect.