Machine Learning Engineer

Analyze business requests and design, develop and optimize data-driven machine learning models.

Collect, clean and prepare data for model training and evaluation.

Evaluate and compare various mathematical and statistical modeling techniques, measuring performance and identifying improvement opportunities.

Implement approved ML models in collaboration with developers and ensure smooth integration into production environments.

Support product teams with mathematical expertise (e.g., creating formulas for new betting features or optimizing existing algorithms).

Provide guidance to internal stakeholders on model usage, interpretation and potential limitations.

Monitor model performance and continuously improve accuracy, scalability and reliability.

Act as a strong analytical partner within a cross-functional team, contributing to product innovation and decision-making.

Strong background in mathematics, statistics or machine learning.

Experience building, evaluating and deploying ML models in production environments, specifically deep learning architectures.

Knowledge of and good awareness on when to use different Neural Network architectures, up to and including the current state of the art.

Proficiency in Python (or similar language) and experience with ML libraries/frameworks.

Experience with data preprocessing, feature engineering and model evaluation techniques.

Understanding of performance metrics and model validation strategies.

Ability to translate business problems into mathematical and technical solutions.

Strong analytical thinking and problem-solving skills.

Fluent in English, both written and spoken.

Experience in betting, gaming, fintech or similar data-intensive industries.

Experience with cloud environments and ML deployment pipelines.

Knowledge of real-time data processing systems.

Experience with generative AI projects – architecture, planning, training, tracking/assessment and iteration.

Experience with working across all steps of a complete data science framework (for ex. CRISP).

MLOps experience on the cloud or on-premises.