Data Scientist

Job description

At Zego, we're on a mission to do the good thing, not the insurance thing.

Insurance hasn't changed much in over a century. The way we live, work and travel has. We're building the real-time, AI-driven infrastructure that powers innovative insurance, so good drivers get cover that works the way they actually drive.

For us, AI isn't a line on a roadmap or a buzzword on a slide. It's our operating reality, and it's how we build products that back drivers instead of the old insurance playbook.

Responsibilities

• Build and refine behavioural features and factors from telematics data.
• Develop and maintain end-to-end pipelines in Python and SQL, ranging from simple heuristics to complex machine learning models.
• Build robust solutions that scale, whether rule-based or ML-driven.
• Collaborate with Software Engineers,Data Scientists, and Product Managers to implement features and evaluate their impact.
• Run experiments, test new ideas, and assist in automating insight generation.
• Translate complex sensor data into clear insights that can shape our roadmap and product.

Requirements

• MSc or BSc in a relevant field (e.g., Computer Science, Engineering, Physics, Statistics), or equivalent practical experience.
• Hands-on experience developing data science or data engineering solutions. You’re comfortable writing production-grade code, not just notebooks.
• Solid skills in Python and SQL, including standard libraries (Polars, Scipy, NumPy, Scikit-Learn).
• Experience building or maintaining data pipelines and tools that are reliable and maintainable.
• Clear communication skills: able to break down problems into testable hypotheses and collaborate effectively within multi-disciplinary teams.
• A growth mindset: curious, eager to learn from senior peers, and open to feedback.
• Exposure to noisy, high-frequency, or time-series sensor data (e.g., GPS, accelerometer).
• Familiarity with digital signal processing or sensor physics principles.
• Familiarity with cloud platforms (e.g., AWS) and tools like Docker or basic pipeline orchestration.
• Background or interest in insurance, mobile telemetry, or behavioural modeling.
• Experience applying Deep Learning models to time-series data.