Applied Data Scientist

Summary

Varonis is seeking a highly skilled and motivated Applied Data Scientist to join their team. The role involves designing, building, evaluating, and deploying machine learning and AI capabilities to enhance cybersecurity detection and response workflows.

Responsibilities

• Apply machine learning, statistical analysis, LLMs, and agent-assisted techniques to solve practical cybersecurity problems across detection, investigation, triage, 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, data engineers, and MLOps teams 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
• Experience applying data science or machine learning to cybersecurity, fraud, risk, abuse, observability, or other adversarial or high-signal/noise domains
• Proven ability to deliver practical, reliable, high-quality AI or ML solutions in production, product, or applied environments
• Proficiency in Python and common data science/ML libraries
• Experience working with LLMs in applied or production contexts, including prompt design, 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
• 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 with PySpark, Databricks, SQL, or large-scale data processing frameworks is a plus

Qualifications

Must Haves

• 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
• Experience applying data science or machine learning to cybersecurity, fraud, risk, abuse, observability, or other adversarial or high-signal/noise domains
• Proven ability to deliver practical, reliable, high-quality AI or ML solutions in production, product, or applied environments
• Proficiency in Python and common data science/ML libraries
• Experience working with LLMs in applied or production contexts, including prompt design, 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
• Strong communication and collaboration skills, with the ability to work effectively across security, engineering, data, product, and research teams

Nice to Haves

• A master's degree is a plus
• Experience with PySpark, Databricks, SQL, or large-scale data processing frameworks is a plus