Data Scientist

A high-growth, well-funded Israeli-global technology scale-up engineering enterprise-grade Cyber Security and intelligent telemetry protection platforms for mission-critical connected ecosystems and large-scale smart mobility platforms.

Backed by premier global venture capital firms, the company provides automated threat detection and behavioral monitoring relied upon by leading global industrial and transport corporations.

The R&D environment is centered on high-throughput distributed data pipelines, complex algorithmic anomaly detection, and an end-to-end squad ownership model.

Located in the Central District directly adjacent to primary train transit, operating on a hybrid model with two days of remote work per week.

Role Description-

• Serving as a Senior Data Scientist, joining a multidisciplinary, end-to-end Data & Applied AI squad alongside Data Engineers to research, develop, and deploy production ML models.
• Taking full End-to-End (E2E) ownership over the algorithmic lifecycle—from data exploration, feature extraction, and mathematical formulation to model training, evaluation, and production deployment.
• Architecting and implementing Machine Learning models to detect anomalies, behavioral drifts, and complex cyber threat patterns across massive automotive telemetry and network datasets.
• Collaborating closely with Data Engineers to design scalable data processing pipelines, optimize data representations, and support low-latency inference workflows.
• Partnering with Cyber Domain Experts and Product teams to translate intricate vehicular protocols and emerging threat vectors into scalable, robust algorithmic features.
• Driving experiment design, evaluation benchmarks, and statistical analysis to continuously monitor and elevate model precision and recall in production.

Requirements

• At least 5 years of proven professional hands-on experience as a Data Scientist developing and deploying Machine Learning solutions – Mandatory
• M.Sc. or Ph.D. in Computer Science, Mathematics, Statistics, Data Science, or an equivalent quantitative academic discipline – Mandatory
• Deep theoretical foundations and proven practical expertise in Machine Learning algorithms, statistical modeling, and predictive analytics – Mandatory
• Extensive, hands-on production programming capabilities in Python and the core scientific/ML ecosystem (e.g., pandas, NumPy, scikit-learn, etc.) – Mandatory
• Proven track record seeing ML models through the full lifecycle end-to-end (from raw data ingestion to production deployment) – Mandatory
• Full-time commitment to working in a hybrid model (3 days on-site in Herzliya Pituach within walking distance of train transit, 2 days remote) – Mandatory
• Fluent, professional technical communication and analytical presentation capabilities in English (written and verbal) – Mandatory