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AI Summary
About the project:
We are hiring for a product-focused iGaming technology company developing advanced real-time personalization solutions for online casino platforms. Their machine learning systems operate directly in production and dynamically shape each player’s experience - including game recommendations, content prioritization, and behavioral targeting - based on live user activity and contextual data.
The team builds scalable ML systems that directly influence business performance, user engagement, and retention across multiple clients in a high-load, multi-tenant environment.
This is a hands-on role combining modeling, experimentation, and production deployment, working closely with engineering and product teams.
Responsibilities:
Design, develop, and improve machine learning models for personalization and recommendation systems
Enhance and maintain production recommendation pipelines across multiple clients
Build models using supervised learning techniques such as regression, classification, and ranking
Improve model performance using gradient boosting methods (e.g., LightGBM, XGBoost, CatBoost) and other ML approaches
Prepare and process behavioral and transactional datasets for modeling
Optimize model training and inference pipelines for performance and scalability
Deploy and integrate ML models into production workflows using Airflow and backend services
Collaborate with engineers, product managers, and data teams to deliver ML-driven product features
Analyze large-scale datasets to generate insights and support product and business decisions
Run experiments, evaluate results, and iterate on models to improve personalization effectiveness
Contribute to adapting and scaling ML solutions across multiple clients (multi-tenant architecture)
Requirements:
5+ years of hands-on experience in Data Science or applied Machine Learning roles
Degree in Mathematics, Statistics, Computer Science, or another quantitative field
Strong Python skills and experience with data processing tools (Pandas, Polars, etc.)
Solid SQL skills and experience working with large datasets
Strong understanding of supervised learning methods, especially regression, ranking, and recommendation systems
Practical experience with gradient boosting models such as XGBoost, LightGBM, or CatBoost
Experience deploying ML models to production environments (real-time or batch pipelines)
Experience working with ML pipelines and production systems
Understanding of experimentation and statistical analysis (A/B testing, hypothesis testing)
Strong analytical thinking and ability to translate business problems into ML solutions
Ability to work cross-functionally with engineering and product teams
Nice to Have:
Experience building or maintaining large-scale recommendation systems in production
Experience with Airflow, Redis/Valkey, or FastAPI
Experience with Docker and Kubernetes
Familiarity with contextual bandits, reinforcement learning, or online optimization methods
Experience with AutoML tools
Experience working in high-load or multi-tenant SaaS environments
Working Conditions:
Fully remote position (Europe-friendly time zone preferred)
Russian-speaking candidates required
Work on a production ML system with real business impact
Flexible remote-first culture, with optional office access in Warsaw
Benefits include:
21 days paid vacation + 5 personal days
Paid sick leave
Medical and wellness compensation
Sports and wellbeing support
Learning and development budget (including language learning support)
Equipment provided and workspace setup compensation
Team events and company gatherings
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