Model Development: Design, build, and validate machine-learning models for use cases such as player churn, LTV forecasting, fraud detection, and game optimisation.
Data Engineering: Work within Snowflake to prepare, transform, and optimise large, complex datasets for modelling and analytics.
MLOps Automation: Implement CI/CD pipelines, containerisation (Docker), and model-monitoring frameworks to ensure production-grade AI deployment.
Collaboration: Partner with game, commercial, and finance teams to translate business problems into measurable ML solutions.
Performance Tracking: Establish KPIs and continuous-improvement loops for models in production.
Documentation & Governance: Maintain robust documentation, version control, and compliance alignment (GDPR, ISO).
Innovation: Research and integrate emerging AI tools and generative approaches relevant to iGaming.

Strong analytical and statistical modelling foundation
Experience with data pipeline design and feature engineering in Snowflake or equivalent platforms
Proven track record of deploying and maintaining ML models in production environments
Familiarity with CI/CD, containerisation, and cloud compute services
Understanding of MLOps best practices and model governance
Ability to translate business goals into technical deliverables
Excellent communication, documentation, and stakeholder-management skills
Curiosity, ownership, and a drive for continuous improvement
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related discipline
Solid hands-on experience in data science or MLOps roles
Demonstrated experience working with Snowflake, Python, and SQL on production-grade projects
Experience in iGaming, entertainment, or digital-product analytics is advantageous.

Data Scientist, AI & MLOps – Mid-level

Key skills