Data Scientist

A cybersecurity startup in the Automotive and Connected Car industries is developing a cloud platform for cybersecurity and data analytics tailored to the automotive sector.

Located in Herzliya, within walking distance of the train station, the company offers a hybrid work model with two work-from-home days per week. With a global team of 115 employees, most of them in Israel.

The company is highly stable, having recently completed a Series C funding round, raising over $100 million. The company enjoys strong backing from strategic investors in the automotive industry and has paying customers, including automakers, insurance companies, and fleet management firms.

Role Overview-

• Join the Data Science team where you will focus on algorithm development and applying Supervised/Unsupervised Learning to solve cybersecurity and quality issues.
• Working with tabular data, combining Sequential/Time series data and Anomaly detection.
• End-to-end involvement, from research to production, including Data Cleansing, Feature Engineering, Model Tuning, and applying skills in Mathematics, Statistics, Machine/Deep Learning, and Data Mining.
• Coding in Python with libraries such as Pandas, Polars, NumPy, SciPy, and Scikit-learn.

Requirements-

• 5 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

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