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.

What will you do: 

As an Associate Data Scientist at CyberArk, you’ll join a team shaping the future of identity security and protecting the emerging agentic AI landscape. You will work alongside experienced data scientists, ML and data engineers, software developers and product managers to research, build, and validate AI/ML solutions that help safeguard the world’s most critical digital assets. 
 
This is an opportunity to launch your data science career by solving real-world problems at scale, while learning from leaders in the cybersecurity and AI space. 

• Research, train and and deploy to production variety of machine learning models, including supervised and unsupervised models and LLM based solutions. 

• Explore and analyze large-scale datasets to uncover patterns and drive actionable insights. 

• Collaborate closely with product managers, engineers, and analysts to support feature development and validate ML ideas. 

• Stay up to date with the latest academic and industry trends in AI/ML and apply best practices to real-world challenges. 

• Grow your skills in classic machine learning, deep learning, natural language processing (NLP), LLMs and cybersecurity applications. 

#LI-Hybrid
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What you need to succeed: 

• MSc in Computer Science, Statistics, Mathematics, Engineering, Data Science or related field (recent graduates welcome). GPA above 88.  

• Solid understanding of machine learning fundamentals and a strong grasp of data structures, algorithms, and probability. 

• Proficiency in Python and familiarity with data science libraries such as Pandas, NumPy, scikit-learn, or similar. 

• 0-2 years of experience. Any hands-on experience is a bonus (personal or course-related projects also count!).   

• Passion for solving meaningful problems and eagerness to learn in a fast-paced, collaborative environment. 

• Strong communication skills and ability to explain technical concepts to different audiences. 

How you will stand out from the crowd:

• Exposure to deep learning frameworks (e.g., PyTorch, TensorFlow) or NLP libraries (e.g., Hugging Face). 

• Experience working with cloud platforms (e.g., AWS, GCP) or distributed data tools (e.g., Spark). 

• Knowledge or interest in cybersecurity – a big plus!