Data Science Engineer

A profitable and globally recognized sports technology company that develops a high-scale mobile application delivering real-time sports updates, deep statistical analysis, rankings, and live data tracking.

Serving hundreds of millions of users worldwide with tens of millions of app downloads and billions of monthly data events, the platform maintains elite user engagement through high-frequency push notifications and dynamic tracking features.

Founded by a prominent group of entrepreneurs, the organization operates a highly collaborative, fast-paced development center driven by a passionate engineering culture.

The company is located in Tel Aviv (train-accessible) and operates on a fully on-site work model.

Key Responsibilities-

• Researching and deploying high-throughput, low-latency machine learning solutions capable of handling billions of behavioral events.
• Formulating and solving multi-variable optimization problems (convex, constrained, and gradient-based) to drive ad-tech, bidding, and dynamic pricing efficiency.
• Applying causal inference, uplift modeling, and advanced experimental design to isolate and measure true business and user impacts.
• Developing production-grade predictive pipelines utilizing high-dimensional time series data for continuous, automated decision-making.
• Implementing robust CI/CD patterns for machine learning, model versioning, experiment tracking, and real-time monitoring of production drifting.

Requirements-

• 5+ years of professional experience operating in Machine Learning or Data Science roles with a proven track record of shipping models to production (Mandatory).
• Deep technical expertise in mathematical optimization, including convex, constrained, or gradient-based methods (Mandatory).
• Proven hands-on experience applying causal inference methodologies, such as propensity scoring, uplift modeling, or rigorous experimental framework design (Mandatory).
• Solid engineering background in time series modeling, Bayesian methods, or hyperparameter optimization applied within resource allocation, pricing, or bidding systems (Mandatory).
• Advanced degree (M.Sc. or Ph.D.) in a highly quantitative discipline like Mathematics, Statistics, Computer Science, or Physics, or equivalent technical industry depth (Mandatory).
• Practical experience establishing and maintaining machine learning lifecycle infrastructure, including experiment tracking, model registries, and production monitoring.