Data Scientist

Ramat Gan, Tel Aviv District, IL

PlaxidityX

Welcome to PlaxidityX , the global leader in vehicle & automotive cyber security. Learn about our innovative car cyber security solutions!

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Description

Join PlaxidityX, a leading force in automotive cybersecurity, where innovation drives our mission to safeguard drivers and manufacturers from cyber threats. As a Senior Data Scientist at PlaxidityX, you'll spearhead transformative initiatives at the forefront of automotive technology.


Why PlaxidityX?

  • Pioneering the automotive cybersecurity landscape with cutting-edge solutions.
  • Empowering millions of lives through revolutionary advancements in safety and security.
  • Immersed in an environment of continuous learning and technological excellence.
  • Opportunity to shape the future of automotive cybersecurity globally.


Responsibilities:

  • Lead the end-to-end development and deployment of machine learning models for anomaly detection and security event analysis.
  • Analyze large-scale security event logs, network traffic, and behavioral data to identify patterns, trends, and potential threats.
  • Design and implement real-time detection algorithms, ensuring they are scalable, efficient, and production-ready.
  • Independently drive data science initiatives, from problem definition to solution deployment, while collaborating with L3 security analysts, data engineers, and SOC teams.
  • Translate business and security challenges into data-driven solutions, proactively identifying opportunities to enhance detection capabilities.
  • Build and maintain robust data pipelines for structured and unstructured security datasets.
  • Work with large-scale unstructured data, including text-based logs and security reports, leveraging NLP, LLMs, and retrieval-augmented generation (RAG) techniques where applicable.
  • Communicate findings and insights clearly to both technical and non-technical stakeholders, ensuring alignment with business and security objectives.
  • Stay up to date with industry trends, continuously improving methodologies, models, and processes.


Requirements

  • 5+ years of experience in machine learning and data science, with the ability to quickly learn and adapt to the cybersecurity domain.
  • Bachelor’s degree in Computer Science, Data Science, or a related field.
  • Strong knowledge of supervised and unsupervised learning techniques, particularly in anomaly detection.
  • Proven ability to work independently, take initiative, and drive projects from concept to production without relying on an established data science team.
  • Hands-on experience with Python, TensorFlow/PyTorch, Scikit-learn, Spark, and SQL.
  • Strong software engineering skills - ability to write production-level ML code and work with MLOps pipelines.

Advantages

  • Practical experience with NLP techniques, including NER and transformer-based models (e.g., BERT, GPT, T5).
  • Familiarity with LLMs, retrieval-augmented generation (RAG), and their real-world applications.
  • Experience with network security, threat modeling, and intrusion detection systems (IDS).
  • Familiarity with SIEM systems and threat intelligence feeds.
  • Advanced academic qualifications (Master's, PhD) in Data Science, Computer Science, or a related field.


At PlaxidityX, you'll be part of a dynamic team dedicated to pushing the boundaries of automotive cybersecurity. If you're passionate about leveraging data science to drive meaningful impact and thrive in a role where innovation is valued and encouraged, we want to hear from you. Join us in shaping the future of automotive security and make your mark on the world.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Job stats:  2  0  0
Category: Data Science Jobs

Tags: BERT Computer Science Data pipelines Engineering GPT LLMs Machine Learning ML models MLOps NLP PhD Pipelines Python PyTorch RAG Scikit-learn Security Spark SQL TensorFlow Unstructured data Unsupervised Learning

Perks/benefits: Career development

Region: Middle East
Country: Israel

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