Machine Learning Engineer

An innovative American AdTech company is expanding its core Data Science team in Israel. The company is highly profitable and operates a high-scale, independent Machine Learning and Data platform integrated directly into global brand ecosystems.

This is a unique opportunity for a researcher to take full end-to-end responsibility for algorithms from initial research to global production with a direct impact on business-critical KPIs.

Role Overview:

• Full Algorithmic Ownership: Leading the research, development, and production deployment of predictive models and recommendation systems at a global scale.
• AdTech Specialization: Building and optimizing Match-Propensity models, Embedding-based systems, and handling "cold-start" challenges within high-volume advertising workflows.
• Predictive Modeling: Implementing advanced Regression, Classification, Clustering, and Ensemble methods (Random Forest, Boosted Trees) to drive personalization and targeting.
• Production ML: Taking models beyond the notebook by integrating them into real-time business logic and measuring performance within complex AdTech ecosystems.
• End-to-End DS Lifecycle: Managing data preprocessing, Feature Engineering, and rigorous model evaluation using Python or R (NumPy, Pandas, SciPy).
• Statistical Rigor: Applying deep knowledge of Linear Algebra, Calculus, and Probability Theory to refine machine learning architectures.

Requirements:

• M.Sc. or Ph.D. in a quantitative field (Computer Science, Statistics, Mathematics, Physics, Economics, or related) – Mandatory.
• 5+ years of professional experience deploying models in a production setting – Mandatory.
• AdTech Domain Expertise: Proven experience in digital advertising, Media platforms, DSP/SSP, or Retail-Media networks – Mandatory.
• Technical Mastery: Deep experience in building and deploying Recommendation Systems and predictive models.
• Proficiency in Python or R: Strong hands-on experience with data manipulation and analysis libraries – Mandatory.
• Solid foundation in Statistical Modeling, Inference, and Dimensionality Reduction.
• Ability to work independently with high levels of accountability in a global, distributed team.

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