As organizations increasingly embrace AI, our new product is designed to help them do so safely, securely, and responsibly. It provides a trusted platform that supports both user-driven and agentic AI—empowering individuals to harness AI for insights and productivity, while enabling autonomous agents to act within clearly defined ethical and operational boundaries. Built with enterprise-grade security, compliance, and transparency at its core, the product ensures that data privacy and governance are never compromised.
We’re looking for a Principal Data Scientist reporting into our Senior Director, Software Engineering. This is a green field opportunity to define and build a new class of market defining products. This is a remote position within the United States. In this role, you will:
- Design, build, and implement machine learning models, including SLMs/LLMs, to enhance the intelligence and functionality of Zscaler’s internal and external platforms.
- Conduct research and development in sophisticated AI/ML areas such as generative AI, predictive analytics, and anomaly detection.
- Analyze complex datasets to extract key insights, continuously refining model accuracy and performance.
- Collaborate with engineering and product teams to effectively integrate AI/ML technologies into Zscaler’s core solutions.
- Stay current with advancements in ML, AI, and cybersecurity, applying specific knowledge of underlying frameworks and methodologies to ensure technical excellence.
- Possess an advanced degree (PhD preferred) in Computer Science, Machine Learning, Statistics, or a related field, combined with a strong understanding of ML algorithms, deep learning, neural networks, and their applications.
- Demonstrated experience (3-5+ years) in applying diverse data science models and advanced Natural Language Processing (NLP) techniques for large-scale data analysis.
- Proficiency in Python and SQL, with hands-on experience using key ML frameworks (e.g., TensorFlow, PyTorch).
- Skilled in data manipulation, visualization, and analysis techniques and tools.
- Proven ability to independently solve complex problems and collaborate effectively within a team environment.
- Possess a strong foundation in mathematics and statistics, applying these to ML and AI, with hands-on experience in large generative models for text and image.
- Proficient in cloud computing platforms (AWS, Azure, GCP), big data technologies (Hadoop, Spark), and data engineering principles, including SQL and NoSQL databases.
- Expertise in deploying scalable machine learning models to production environments, complemented by knowledge of advanced AI protocols such as A2A, Agentic AI, and MCP.