A startup company in the Cybersecurity for Automotive domain is developing a Cloud-based platform for cybersecurity and data analytics for the automotive industry.

Based in central Israel, within walking distance from a train station, and offering a flexible hybrid model with two days of work from home. The company has 115 employees globally, most of whom are based in Israel.

Having recently completed a Series C round, the company has raised over $100M to date and is financially stable moving forward. It is backed by strategic investors from the automotive industry and already serves paying customers including car manufacturers, insurance companies, and fleet management providers.

About the role-

• Part of the Data Science team, focusing on algorithm development for cybersecurity and quality issues
• Work on Supervised and Unsupervised learning tasks, primarily using structured data
• Emphasis on Sequential / Time Series data and Anomaly Detection
• Develop in Python using libraries such as Pandas, Polars, NumPy, SciPy, and Scikit-learn
• End-to-end involvement: from research to production deployment

Responsibilities include-

• Data Cleansing
• Feature Engineering
• Model Tuning
• Applied Mathematics and Statistics
• Machine Learning and Deep Learning
• Data Mining

Requirements-

• 6+ years of relevant industry experience in data science or machine learning
• Education: MSc in Math / EE / CS / Statistics OR BSc in one of the disciplines above and 10+ years of substantial, hands-on experience in data science or a closely related domain – demonstrating expertise equivalent to that typically acquired through formal advanced education
• Demonstrated research expertise in the tech industry or published academic papers
• Profound understanding and hands-on expertise in mathematics, statistics, machine/deep learning, and data mining
• Expertise in data science pipelines is essential, including data cleansing, feature engineering, and model tuning
• Strong coding and software engineering experience focusing on advanced proficiency in Python, especially in data science libraries such as Pandas, Polars, NumPy, SciPy, Scikit-learn, and others
• End-to-end experience in leading the development of AI/DS/ML

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