I work across data analysis, business intelligence, machine learning, applied AI, and automation. I enjoy taking an open-ended problem, understanding the data behind it, and building a result that people can use - whether that is a dashboard, a forecasting model, an analytical study, or an AI-assisted workflow.
- Data Specialist at Check Point Software - analytics, reporting, data quality, dashboards, and automation.
- Former AI Engineer Intern at Checkmarx / CodeMatch - LLM embeddings, vector search, benchmark data, and model evaluation.
- B.Sc. in Digital Sciences and Economics, Tel Aviv University - 2024 Dean's List.
- Based in Israel and open to conversations across data, analytics, AI, and technology.
| Analytics & BI | Machine Learning & Statistics | Applied AI & Automation | Software & Collaboration |
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| Dashboards, KPIs, reporting, data quality | Classification, forecasting, recommenders, regression | LLMs, embeddings, vector search, Python automation | Git, algorithms, OOP, C#, team leadership |
Developed an AI-driven system that identifies semantic similarity between code snippets beyond basic text matching. The project uses LLM embeddings, vector search, benchmark datasets, and model evaluation to represent the meaning of source code, retrieve related snippets efficiently, and assess result quality.
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Forecasted ride demand near the Empire State Building using time, weather, events, and location signals. Compared ensemble models, with CatBoost achieving the lowest RMSE.
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Built personalized top-10 recommendations from more than two million interactions using collaborative filtering, content-based methods, and matrix factorization.
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Created a binary-classification pipeline using static file features, exploratory analysis, feature preparation, model training, and reliability evaluation.
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Explored pricing, occupancy, neighborhoods, host behavior, and revenue efficiency to turn marketplace data into practical business insights.
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Applied NLP and deep-learning workflows to classify dialogue from Friends and Seinfeld, including preprocessing, representation, and model comparison.
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Predicted daily bicycle demand from weather, temperature, season, and time features using optimized ensemble-regression models.
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More project work
- Image Processing — Python and OpenCV implementations of filtering, transformations, histogram analysis, and feature extraction.
- Sweden Education Reform — Difference-in-differences analysis of education reform, schooling, and earnings.
- Happiness Data Analysis — Statistical analysis, hypothesis testing, regression, and global development indicators.
- Bubble Trouble — A C# and MonoGame project with OOP design, power-ups, and AI-driven bot behavior.
- Google AI Essentials Specialization — practical generative-AI workflows and responsible use.
- AWS Academy Cloud Developing — cloud application development and architecture foundations.
- Microsoft Certified: Azure AI Fundamentals — AI workloads, machine learning, responsible AI, and Azure services.
Interested in data, analytics, AI, or building useful technology?
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