Data Scientist

A young startup in the Sales and e-Commerce AI space is developing an AI-powered product for e-Commerce companies, transforming data into actionable insights that drive revenue growth. Their solutions integrate seamlessly with existing systems to optimize key business metrics, whether it's increasing purchase frequency, reducing customer churn, or enhancing customer retention.

The goal is to help businesses reach their full potential by leveraging AI-driven insights to send personalized messages, engage customers, and boost sales and retention.

Located in Tel Aviv, near the train line, the company has 6 employees and operates a hybrid model with one work-from-home day per week.

The company has been around for almost a year and has secured a significant pre-seed round with funding for the next two years.

Responsibility

• Collaborate with cross-functional teams (engineers, founders, product) to develop, test, and deploy machine learning models quickly and effectively.

• Analyze and interpret complex data sets related to transactions and activity logs.

• Build regression, classification, and clustering models using Machine Learning and/or Deep Learning algorithms and techniques.

• Continuously improve models through retraining, optimization, and performance monitoring.

Requirements-

• 4 years of professional experience as a data scientist, with a strong background in machine learning working with tabular data.

• Track record of bringing AI-based products from the POC stage to production at scale.

• Entrepreneurial mindset: capable of taking ownership of the solution, working independently, and bringing innovative ideas to the team.

• Proven experience in building and applying time series and classification models for churn and prediction analysis.

• Experience with implementing end-to-end deep learning pipelines, including data preparation, retraining, validation and deployment.

• Proficiency in Python including experience with Data Science frameworks such us: NumPy, Pandas, Scikit-Learn, XGBoost, Keras, Tensorflow, PyTorch

• Proven experience in deploying models in a production environment.

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