Data Scientist

A high-impact AutoTech and Connected Mobility AI company developing an intelligent telematics and predictive analytics platform for the global automotive industry.

By ingesting and analyzing billions of real-time vehicular data signals, the platform enables real-time anomaly detection, predictive maintenance, and driver behavioral profiling for global automotive OEMs, Tier-1 suppliers, and fleet operators.

Operating its core R&D center in Israel, the organization combines massive Big Data streaming infrastructure with production-grade Machine Learning and Agentic AI systems.

Located in the Sharon region (Herzliya, walking distance from the train station), operating on a hybrid work model with 1 day WFH per week.

Role Description-

• Serving as a Senior Machine Learning / AI Production Engineer, taking full end-to-end ownership of the ML lifecycle—from exploratory data analysis and custom architecture modeling to high-throughput production deployment and ongoing operations.

• Processing and modeling billions of noisy, high-velocity, real-time vehicular telemetry data points to build robust predictive maintenance and behavioral models.

• Designing, training, and optimizing custom deep learning architectures in PyTorch, transitioning complex prototypes into scalable, testable production services.

• Taking complete operational ownership of models in Production, including continuous monitoring, automated retraining pipelines, drift detection, and live debugging under robust CI/CD frameworks.

• Developing and deploying production-grade LLM and Agentic AI applications, implementing evaluation frameworks (Evals), guardrails, latency optimization, and cost-governance mechanisms.

• Collaborating cross-functionally with Big Data Engineers, Backend Architects, and Automotive Product domain experts.

Requirements-

• 5+ years of hands-on industry experience as a Machine Learning Engineer or Data Scientist – Mandatory

• Proven track record of taking ML / Deep Learning models fully into Production with ongoing operational ownership (monitoring, drift mitigation, retraining, debugging; prototype-only experience is not sufficient) – Mandatory

• Deep proficiency in Python, with proven experience authoring tested, enterprise-grade production modules and/or owning microservices under CI/CD – Mandatory

• Extensive hands-on experience building and training custom architectures using PyTorch and deploying them to production – Mandatory

• Solid hands-on experience working within Big Data Platforms & distributed data infrastructures – Mandatory

• Direct ownership of Production Engineering workflows / MLOps pipelines – Mandatory

• Hands-on experience deploying LLMs or Agentic AI applications in Production (including evaluations, guardrails, and latency/cost tuning) – Significant Advantage

• Practical experience working with Noisy, Unstructured, or Weakly-Labelled Data / Time-Series Telemetry – Significant Advantage