Data Scientist

An established, high-profile Sports-Tech and digital media company operating as an independent innovation hub within a leading global sports entertainment and gaming group.

The engineering and data teams operate at massive global scale, pairing tier-1 international corporate backing with deep computational, algorithmic, and data-engineering challenges.

The office is located in Tel Aviv adjacent to the train station.

Role Description-

• Serving as a Senior Machine Learning / Optimization Scientist, joining the core Machine Learning & AI group to design and deploy high-impact mathematical and machine learning models for production systems.
• Developing and applying mathematical optimization frameworks spanning convex optimization, constrained optimization, and gradient-based methods to solve complex real-world operational challenges.
• Implementing advanced Bayesian Optimization and hyperparameter tuning techniques to refine dynamic algorithms and decision models.
• Designing and implementing Propensity Scoring, Uplift modeling, and rigorous Experimental Design (A/B testing frameworks) to measure and maximize algorithmic impact.
• Building predictive and prescriptive ML models for commercial optimization domains, including dynamic pricing, automated bidding, and resource allocation.
• Architecting advanced Time Series forecasting models supporting low-latency, real-time dynamic decision-making engines.
• Driving the end-to-end ML lifecycle from mathematical formulation, research, and prototyping through to real-time deployment, monitoring, and scaling in mission-critical production environments.

Requirements-

• 5+ years of hands-on experience in Machine Learning engineering / Data Science roles with proven, tangible impact in large-scale production environments – Mandatory
• Deep theoretical and practical expertise in Mathematical Optimization: convex optimization, constrained optimization, and gradient-based approaches – Mandatory
• Hands-on proficiency in Bayesian Optimization or algorithmic hyperparameter optimization – Mandatory
• Strong practical skills in causal inference and experimentation: Propensity Scoring, Uplift Modeling, and Experimental Design – Mandatory
• Proven track record applying ML and optimization methods to operational business domains: Pricing algorithms, Real-Time Bidding (RTB), or automated Resource Allocation – Mandatory
• Solid background developing and evaluating advanced Time Series architectures for dynamic, real-time decisioning – Mandatory
• Demonstrated hands-on experience in deploying, serving, and monitoring low-latency ML models in production (Real-Time / Streaming) – Mandatory
• Advanced degree (M.Sc. / Ph.D.) in Computer Science, Applied Mathematics, Operations Research, Statistics, or a related quantitative field – Significant Advantage