Job VC
Data Scientist
Technologies
Description
Domain:
iGaming / Gambling
Format:
Product company
Experience:
5+ years
About the Company
We are an international product company operating in the gambling sector. Our platform delivers real-time personalization for casino and sportsbook products using advanced machine learning. The solution processes live behavioral, transactional, and contextual data to improve player engagement, retention, and overall performance for operators worldwide.
Our focus is on building production-grade ML systems that directly influence what users see in real time — from game recommendations to personalized content and promotions.
Role Overview
We are looking for a
Senior Data Scientist
to join a product-focused team working on real-time personalization and recommendation systems for iGaming platforms.
This is a hands-on role that combines modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time production environment.
Main Responsibilities
Develop ML-driven features for casino products using supervised learning (regression, ranking, classification)
Maintain and improve existing recommendation systems in production
Enhance models using gradient boosting and other supervised approaches
Perform data cleaning, preprocessing, and feature engineering
Design and maintain pre- and post-processing workflows
Optimize training and inference pipelines for performance and reliability
Integrate ML models into Airflow pipelines in a multi-tenant environment
Adapt and configure the solution for different clients (tenants)
Collaborate closely with product and engineering teams on experimentation and feature delivery
As Part of the Team You Will
Work cross-functionally with data scientists, engineers, product owners, designers, and researchers
Analyze large-scale datasets to extract insights for product and business decisions
Propose, implement, and evaluate ML approaches to solve real business problems
Support and evolve a recommendation solution used across multiple tenants
Influence product strategy through research, experimentation, and data-driven insights into user behavior
Experience & Education
5+
years of professional experience in data science
Degree in a quantitative field (Mathematics, Statistics, Computer Science, or similar)
Core Skills
Strong proficiency in
Python
and
SQL
Hands-on experience with data processing tools (
Pandas, Polars
)
Solid engineering skills for building and maintaining scalable
ML systems
Experience implementing observability in
ML pipelines
(metrics, logging, alerting)
Knowledge of
Docker and Kubernetes
Strong analytical mindset with the ability to solve loosely defined problems
Hands-on experience with supervised ML techniques, including:
Regression and ranking models (XGBoost, LightGBM, CatBoost, neural networks)
Feature engineering and model evaluation (AUC, NDCG, MSE, uplift metrics)
Personalization or recommendation systems
Proven experience deploying ML models to production (near real-time or batch)
Solid understanding of statistical methods (A/B testing, significance testing)
Nice to Have
Production experience with large-scale recommendation systems
Experience with Airflow, Valkey/Redis, FastAPI in production
Familiarity with contextual bandits or reinforcement learning
Experience with AutoML tools
iGaming / Gambling
Format:
Product company
Experience:
5+ years
About the Company
We are an international product company operating in the gambling sector. Our platform delivers real-time personalization for casino and sportsbook products using advanced machine learning. The solution processes live behavioral, transactional, and contextual data to improve player engagement, retention, and overall performance for operators worldwide.
Our focus is on building production-grade ML systems that directly influence what users see in real time — from game recommendations to personalized content and promotions.
Role Overview
We are looking for a
Senior Data Scientist
to join a product-focused team working on real-time personalization and recommendation systems for iGaming platforms.
This is a hands-on role that combines modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time production environment.
Main Responsibilities
Develop ML-driven features for casino products using supervised learning (regression, ranking, classification)
Maintain and improve existing recommendation systems in production
Enhance models using gradient boosting and other supervised approaches
Perform data cleaning, preprocessing, and feature engineering
Design and maintain pre- and post-processing workflows
Optimize training and inference pipelines for performance and reliability
Integrate ML models into Airflow pipelines in a multi-tenant environment
Adapt and configure the solution for different clients (tenants)
Collaborate closely with product and engineering teams on experimentation and feature delivery
As Part of the Team You Will
Work cross-functionally with data scientists, engineers, product owners, designers, and researchers
Analyze large-scale datasets to extract insights for product and business decisions
Propose, implement, and evaluate ML approaches to solve real business problems
Support and evolve a recommendation solution used across multiple tenants
Influence product strategy through research, experimentation, and data-driven insights into user behavior
Experience & Education
5+
years of professional experience in data science
Degree in a quantitative field (Mathematics, Statistics, Computer Science, or similar)
Core Skills
Strong proficiency in
Python
and
SQL
Hands-on experience with data processing tools (
Pandas, Polars
)
Solid engineering skills for building and maintaining scalable
ML systems
Experience implementing observability in
ML pipelines
(metrics, logging, alerting)
Knowledge of
Docker and Kubernetes
Strong analytical mindset with the ability to solve loosely defined problems
Hands-on experience with supervised ML techniques, including:
Regression and ranking models (XGBoost, LightGBM, CatBoost, neural networks)
Feature engineering and model evaluation (AUC, NDCG, MSE, uplift metrics)
Personalization or recommendation systems
Proven experience deploying ML models to production (near real-time or batch)
Solid understanding of statistical methods (A/B testing, significance testing)
Nice to Have
Production experience with large-scale recommendation systems
Experience with Airflow, Valkey/Redis, FastAPI in production
Familiarity with contextual bandits or reinforcement learning
Experience with AutoML tools