Senior Data Scientist

We are looking for a Senior Data Scientist on behalf of our client – an early-stage Israeli startup revolutionizing the real estate investment space through AI and alternative data. The company is building an intelligent platform that helps investors identify high-return opportunities at the ZIP-code level using predictive analytics, risk modeling, and automated investment strategies.

This is a hands-on role where you’ll work directly with large-scale, unconventional data sources—from mobility signals to social trends—to build models that forecast property values, rental income, and market shifts before they become obvious to the broader market. You’ll collaborate with engineering and product teams to make data science a core driver of the platform’s success.

Key Responsibilities:

• Design and build predictive models for real estate pricing, rent forecasting, and investment potential at the micro-geographic level (ZIP code).

• Analyze and integrate alternative datasets such as location mobility, social indicators, and macroeconomic data to improve model accuracy.

• Develop, train, and validate machine learning models (XGBoost, linear regression, neural networks, time-series forecasting, etc.).

• Work closely with engineers to integrate models into the production environment.

• Build internal dashboards and data visualization tools to help investors make data-backed decisions.

• Contribute to shaping the overall data infrastructure and tooling.

• Collaborate with company leadership to ensure data insights are embedded in key business decisions.

Requirements:

• 5+ years of experience in Data Science, Machine Learning, or AI.

• Strong proficiency in Python (Pandas, NumPy, Jupyter) and data manipulation techniques.

• Solid SQL skills – writing optimized queries, complex joins, aggregations.

• Experience building and deploying supervised machine learning models.

• Knowledge of time-series forecasting and working with real-world, noisy data.

• Ability to communicate complex analytical insights to both technical and non-technical stakeholders.

• English at a high professional level.

Bonus Points For:

• Experience with geospatial data (GeoPandas, PostGIS, Kepler.gl).

• Background in real estate, urban economics, or working with financial/investment data.

• Familiarity with MLOps tools and best practices.

• Startup experience – ability to move fast and iterate with limited resources.