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