At 365Scores, we're revolutionizing how fans receive and interact with live sports updates and data. Our platform combines the intensity of live sports with cutting-edge technology to deliver a personalized experience to millions of users worldwide.

As a Senior Data Scientist at 365Scores, you will be an integral part of our Machine Learning and AI team. The role is hands-on and impact-driven, focusing on building, improving, and delivering machine learning models that directly support business goals.

Responsibilities:

• Deep expertise in mathematical optimization (convex optimization, constrained optimization, gradient-based methods)
• Hands-on experience with Bayesian optimization, hyperparameter optimization, or similar techniques
• Strong foundation in causal inference (propensity scoring, uplift modeling, causal ML, or experimental methods)
• Advanced time series modeling and forecasting for decision-making systems
• Proven ability to architect and train deep neural networks (PyTorch or TensorFlow) for complex problems
• Experience applying ML/DL to optimization problems (pricing, bidding, resource allocation, sequential decisions)
• Track record of improving model performance through rigorous experimentation
• Demonstrated success deploying and maintaining ML models in real-time production environments
• Experience building end-to-end ML pipelines from data ingestion to model serving to monitoring
• Proficiency with MLOps practices: experiment tracking, model versioning, A/B testing, performance monitoring
• Expertise with model serving at scale and handling production incidents
  
  

Requirements:

• 5+ years of experience with demonstrated impact in production ML systems
• Experience with LLMs and agentic AI systems
• Reinforcement learning for dynamic decision-making
• Prior work on pricing optimization, revenue optimization, or real-time bidding systems
• Contributions to open source ML projects or publications in relevant areas
• Dynamic optimization systems that operate in production (pricing, bidding, allocation)
• ML systems where predictions drive automated business decisions
• Real-time models that adapt based on incoming data and feedback
• End-to-end ownership of ML projects from problem formulation to production impact
• Advanced degree (MS/PhD) in quantitative field, or equivalent depth through industry experience
• Translates ambiguous business problems into rigorous mathematical frameworks
• Owns projects independently from conception through production deployment
• Mentors other team members on ML best practices