AI Agents Data Science Team Leader
Job Description
- Familiarity with cybersecurity, identity security, or risk mitigation use cases.
- Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, GCP Vertex AI).
- Experience with big data tools (e.g., Spark, Airflow).
- Hands-on knowledge of MLOps tooling for CI/CD, monitoring, and versioning of ML assets.
- Publications, patents, or open-source contributions in AI/ML.
- Lead, mentor, and grow a team of applied data scientists working on machine learning models.
- Own the end-to-end development lifecycle of AI projects from research to deployment.
- Partner with engineering and product leadership to define technical strategies and prioritize initiatives.
- Drive innovation across ML domains including supervised/unsupervised learning, anomaly detection, and generative AI.
- Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
- Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
- Champion best practices in MLOps, data governance, explainability, and monitoring.
- 6+ years of industry experience in AI/ML or Data Science, with at least 1-year leading teams.
- Master’s degree or PhD in a technical field (e.g., Computer Science, Machine Learning, Statistics, Engineering).
- Solid proficiency in Python and machine learning libraries/frameworks.
- Strong familiarity with natural language processing (NLP), including LLMs and transformer-based architectures.
Company Description
About CyberArk:
CyberArk (NASDAQ: CYBR), is the global leader in Identity Security. Centered on privileged access management, CyberArk provides the most comprehensive security offering for any identity – human or machine – across business applications, distributed workforces, hybrid cloud workloads and throughout the DevOps lifecycle. The world’s leading organizations trust CyberArk to help secure their most critical assets. To learn more about CyberArk, visit our CyberArk blogs or follow us on X, LinkedIn or Facebook.
Job Description
As a Data Science Team Leader at CyberArk, you will lead a high-performing team developing AI/ML solutions that secure the digital world’s most sensitive assets. You’ll play a key role in driving innovation, productizing machine learning systems, and applying advanced modeling techniques to real-world cybersecurity challenges. In this leadership role, you’ll balance hands-on technical involvement with strategic oversight, guiding projects from research to production. You’ll work closely with data scientists, engineers, analysts, product managers, and Cyber-Security researchers to build intelligent features across CyberArk’s identity security portfolio.
- Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
- Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
- Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
- Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
- Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
- Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
- Champion best practices in MLOps, data governance, explainability, and monitoring.
- Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.
#LI-Hybrid
#LI-OS1
Qualifications
- 6+ years of industry experience in AI/ML or Data Science, with at least 1-year leading teams and managing direct reports.
- Master’s degree or PhD in a technical field (e.g., Computer Science, Machine Learning, Statistics, Engineering).
- Solid proficiency in Python and machine learning libraries/frameworks such as scikit-learn, PyTorch, TensorFlow, or Hugging Face.
- Strong familiarity with natural language processing (NLP), including LLMs and transformer-based architectures, and their applications to real-world use cases.
- Strong technical communication skills and the ability to collaborate across cross-functional teams.
- Strategic mindset with the ability to connect AI research to business value and product opportunities.
Additional Information
- Familiarity with cybersecurity, identity security, or risk mitigation use cases.
- Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, GCP Vertex AI) and big data tools (e.g., Spark, Airflow).
- Hands-on knowledge of MLOps tooling for CI/CD, monitoring, and versioning of ML assets.
- Publications, patents, or open-source contributions in AI/ML.
