Quantitative Analyst

About Kambi

Kambi Group plc is a leading provider of premium sports betting services to licensed gaming operators worldwide.

We deliver an end-to-end solution for operators wanting to launch a standalone Sportsbook or enhance their existing offering with a cutting-edge sports betting product.

Our comprehensive services include front-end user interface, customer intelligence, risk management, and odds compiling – all built on our in-house developed software.

We strive to provide the ultimate service and solution to our partners, enabling them to create exceptional sports betting experiences.

At Kambi, our vision is to be the world's leading provider of sports betting services, working together with our partners to achieve this goal.

About Tzeract

Welcome to Tzeract – a startup within Kambi Group comprising over 20 engineers who are revolutionizing sports betting with AI, advanced analytics, and real-time data.

We're a small, hybrid team that creates innovative technology powering next-generation betting experiences.

With Kambi's global resources behind us, we offer the opportunity to work on complex mathematical projects that scale across international markets.

Learn more about us at www.tzeract.com.

• Develop and refine models that drive AI-powered odds compiling.
• Analyze sports data using machine learning and simulations.
• Contribute to creating reliable, well-structured code and optimizing systems for performance.

This role requires collaboration with the engineering team, integrating algorithms into production systems, and ensuring they operate efficiently at scale.

You'll design, develop, and implement advanced mathematical models and machine learning algorithms for predictive analytics in sports betting.

Statistical analysis will help you understand data patterns, validate models, and enhance accuracy.

Work with large-scale, real-time, and historical sports data sets, applying quantitative techniques to extract actionable insights.

Communicate complex quantitative analyses and concepts to non-technical team members to inform strategy decisions.

• Master's degree in Mathematics, Statistics, Computer Science, Physics, Engineering, or a related quantitative field.
• Experience in mathematical/statistical modeling and machine learning.
• Proficiency in Python, with experience in libraries like NumPy and Pandas.
• Familiarity with TensorFlow, PyTorch, SciPy, or similar machine learning frameworks.
• Ability to describe reality through mathematics and implement solutions into practical applications.
• Able to multitask different roles – whether it's tackling complex math problems or contributing to development tasks.

Please note that relocation packages are not supported.