Senior Data Scientist

Role Description

At Zingbrain, we build real-time personalization systems for iGaming platforms. Our models operate in production, influencing what each user sees — from game recommendations to sportsbook event suggestions — based on live behavioral, transactional, and contextual data.

We’re looking for a Senior Data Scientist to join our team and help us in the following areas:

• Develop ML-driven features for casino games using supervised learning (regression, ranking, classification)
• Maintain and enhance the existing recommendation systems in production, including:
  • Model enhancement using gradient boosting methods
  • Data cleaning and preprocessing
  • Pre- and post-processing workflows
  • Optimization of training and inference pipelines
  • Integration of ML models into Airflow pipelines in a multi-tenant environment
  • Adopt and configure the solution for different clients (tenants)

This is a hands-on role involving modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time environment.

As a part of our team you will:

• Collaborate with cross-functional teams of data scientists, engineers, product owners, designers, and researchers to ensure project success
• Analyze large datasets to extract actionable insights that inform product decisions
• Propose, implement, and evaluate machine learning approaches to solve business problems, working closely with Product Owner(s)
• Maintain and adopt the current recommendation solution in a multi-tenant environment
• Influence product strategy through research and experimentation that deepens understanding of how product features, platforms, and promotions affect user behavior

Qualifications

• 5+ years of experience in data science
• A degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science)

Requirements

• Proficiency in Python, SQL, and data manipulation tools (Pandas, Polars)
• Strong engineering skills to design, build, and maintain scalable ML solutions, including implementing observability across pipelines through metrics, logging, and alerting
• Knowledge of Docker, Kubernetes
• Ability to structure and solve loosely defined problems, delivering actionable insights for product development; Strong analytical mindset with both numerical and business understanding
• Hands-on experience with supervised ML techniques (regression and ranking using XGBoost, LightGBM, CatBoost, or neural networks), including feature engineering, model evaluation (AUC, NDCG, MSE, uplift metrics), and personalization or recommendation systems
• Proven experience deploying ML models to production for near real-time or batch processing
• Solid knowledge of statistical methods (A/B testing, significance testing, etc.)

Benefits

• Wellness program
• Medical compensation
• Paid sick leaves
• Compensation for sports activities
• Well-being webinars and workshops
• Work & life balance
• Wellness Day: 4th Friday off monthly
• Remote work
• 21 working days of vacation
• 5 personal days per year
• Professional development
• English speaking club
• Language learning bonus €150 per month
• 80% paid professional employee training
• Provided tech equipment
• €150 for the arrangement of the workplace
• Bonuses for significant events and additional personal days if necessary
• Offline and online company parties and team buildings