Note: The job is a remote job and is open to candidates in USA. Varonis is seeking an Applied Data Scientist to develop AI-powered capabilities for cybersecurity products. The role focuses on applying machine learning, statistical analysis, LLMs, and agentic AI to improve threat detection, investigation, and response workflows, while partnering with engineering and security teams to productionize and monitor solutions.
Responsibilities
- Apply machine learning, statistical analysis, LLMs, and agent-assisted techniques to solve practical cybersecurity problems across detection, triage, investigation, and response
- Analyze large, complex security datasets to identify behavioral patterns, anomalies, attack signals, and trends that can improve threat detection and analyst workflows
- Translate real-world cybersecurity use cases into data science problems, including problem framing, dataset creation, feature development, model selection, evaluation, and iteration
- Design, develop, and evaluate ML and LLM powered security workflows, including retrieval, reasoning, tool use, human-in-the-loop review, feedback loops, and guardrails
- Build evaluation frameworks, metrics, benchmarks, and test datasets to measure model quality, reliability, precision, recall, latency, robustness, and operational impact
- Develop prompts, instructions, retrieval strategies, and model interaction patterns that improve the usefulness, consistency, and safety of LLM-powered features
- Partner with software engineers to productionize models, AI agents, and data pipelines in secure, scalable, and maintainable systems
- Monitor deployed models and workflows for performance drift, data quality issues, false positives, false negatives, and opportunities for continuous improvement
- Collaborate with cybersecurity researchers, threat analysts, and product stakeholders to ensure AI capabilities address real user needs and evolving threat scenarios
- Translate relevant advances in ML, LLMs, agentic AI, and cybersecurity into practical product improvements, evaluation methods, and internal best practices
Skills
- Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, or a related field
- Strong hands-on foundation in machine learning, applied statistics, and data science, including supervised learning, unsupervised learning, anomaly detection, model evaluation, and experimentation
- 3+ years of experience applying data science or machine learning in production or applied settings
- Proficiency in Python and common data science/ML libraries
- Experience working with LLMs in applied or production contexts, including prompt engineering, model selection, evaluation, retrieval-augmented generation, and safe deployment
- Familiarity with embeddings, vector databases, retrieval systems, and RAG-based workflows for security, knowledge-intensive, or analyst-facing applications
- Understanding of AI and LLM security considerations, including adversarial inputs, prompt injection, data privacy, model misuse, governance, and safe system design
- Experience partnering with engineering teams to deploy, monitor, and improve ML models, AI workflows, or data products in production environments
- Ability to reason under uncertainty, work with noisy and incomplete data, and make pragmatic tradeoffs between model performance, explainability, latency, reliability, and operational value
- Demonstrated ability to independently frame ambiguous problems, develop, and validate solutions, and drive them from experimentation through production and measurable impact
- Strong communication and collaboration skills, with the ability to work effectively across security, engineering, data, product, and research teams
- A master's degree is a plus
- Experience in cybersecurity, fraud, risk, abuse, observability, or other adversarial or high-noise domains is strongly preferred
- Experience with PySpark, Databricks, SQL, or large-scale data processing frameworks is a plus
Benefits
- Hybrid work arrangement
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