Note: The job is a remote job and is open to candidates in USA. Varonis is seeking a highly skilled and motivated Applied Data Scientist to join their team. In this hands-on role, you will design, build, evaluate, and deploy machine learning and AI capabilities to enhance cybersecurity 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
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