Applied Data Scientist

Title: Applied Data Scientist

Job Description

We are seeking a highly skilled and motivated Applied Data Scientist to join our

team.

In this role, you will

• design, build, evaluate, and deploy ML-, LLM-, and
• agent-assisted capabilities that improve real-world cybersecurity detection,
• investigation, and response workflows.
• You will work closely with architects, data engineers, software engineers, data
• scientists, security researchers, and threat analysts to turn complex security
• data and attack scenarios into reliable, scalable, and measurable AI-powered
• product capabilities. This is a hands-on applied role focused on solving
• practical cybersecurity problems using data science, machine learning, LLMs, and
• modern AI systems.

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.

Requirements

• * 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.
• #LI-Hybrid
• We invite you to check out our Instagram Page to gain further insight into the
• Varonis culture!
• @VaronisLife
• Varonis is an equal-opportunity employer. We evaluate qualified applicants
• without regard to race, color, religion, sex, national origin, disability,
• veteran status, and other legally protected characteristics