Note: The job is a remote job and is open to candidates in USA. Varonis is a company focused on cybersecurity solutions, and they are seeking a highly skilled Applied Data Scientist to join their team. The role involves designing, building, evaluating, and deploying machine learning 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. A master's degree is a plus
• 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 with PySpark, Databricks, SQL, or large-scale data processing frameworks is a plus
• 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
• Experience with PySpark, Databricks, SQL, or large-scale data processing frameworks is a plus

Company Overview

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