Data Science Engineer

An innovative, profitable American Ad-Tech company operating a high-scale global data and machine learning ecosystem embedded directly within international brand platforms.

With a growing global workforce, the company is strategically establishing its core data science division in Israel as part of an elite international team.

The organization offers an exceptional Fully Remote work model, running on local Israeli business hours, and maintains a mature, low-politics culture focused on high professional standards, deep technical autonomy, and direct business impact.

Key Responsibilities-

• Researching, designing, and implementing production-ready machine learning algorithms for large-scale classification, regression, and clustering challenges.
• Constructing robust feature engineering pipelines, high-dimensional embeddings, and dimensionality reduction strategies.
• Conducting deep statistical modeling and exploratory data analysis across high-volume, multi-source global datasets.
• Deploying, optimizing, and monitoring live ML models to ensure microsecond-level inference latency and high behavioral prediction accuracy.
• Evaluating model drift, tuning hyperparameters, and continuously refining core algorithmic infrastructure.
• Technical Environment -Data Science, Machine Learning, Statistical Modeling, Predictive Modeling, Recommendation Systems, Personalization, Feature Engineering, Embeddings, Regression, Classification, Clustering, Decision Trees, Random Forest, Dimensionality Reduction, Big Data, Python, R, NumPy, Pandas, SciPy, and Production ML.

Requirements

• M.Sc. or Ph.D. degree in Computer Science, Statistics, Mathematics, Physics, Economics, or a highly related quantitative scientific discipline (Mandatory).
• 5+ years of professional experience as a Data Scientist developing, testing, and scaling advanced machine learning systems in product-centric environments (Mandatory).
• Deep, comprehensive hands-on expertise building and deploying diverse Machine Learning frameworks and statistical algorithms (Mandatory).
• Strong programming proficiency in Python or R, alongside extensive experience utilizing core data science libraries (e.g., NumPy, Pandas, SciPy).
• Proven track record of taking algorithmic models into full Production environments within

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