Senior GenAI Data Scientist

ActiveFence

. Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks .

Posted 10/5/2026full-timeRamat Gan • IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models and systems, particularly in the context of adversarial testing and GenAI red teaming. Proficient in data analysis, statistical techniques, and the development of evaluation pipelines to ensure model accuracy and performance.

Highest-signal resume keywords

Python ProgrammingMachine Learning ExperienceGenAI Red TeamingScikit-LearnPyTorch

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceSQLStatistical TechniquesModel EvaluationClusteringNLPAnomaly DetectionClassification SystemsExperimental DesignPrompt Engineering

Tools & Technologies

CradleApache HadoopSparkETL PipelinesCI/CD

Certifications & Qualifications

M.Sc. in CS/EE/Math/Statistics

Industry Keywords

Adversarial AttacksLLM SecurityData AnalysisMulti-Step WorkflowsModel Behavior Tracking

Tech Stack

Tools & technologies

ApacheETLHadoopPythonPyTorchScikit-LearnSparkSQL

About the role

Key responsibilities & impact

Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks
Manage and analyze prompt and response data from red-team campaigns, model evaluations, and synthetic attacks; clean, curate, normalize, and label it
Analyze structured and unstructured data to uncover trends, clusters, and anomalies, including attack families, jailbreak techniques, and previously unseen patterns
Develop ML models and predictive algorithms to automate red-teaming
Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers
Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility
Take research to production by building reliable, scalable systems optimized for cost and latency, with testing, CI/CD, and telemetry

Requirements

What you’ll need

5+ years of hands-on data science or ML experience, programming in Python or R, and SQL
3+ years of experience with Scikit-Learn and PyTorch
Hands-on experience in GenAI red teaming, adversarial testing, or LLM security
Hands-on experience building and deploying LLM-powered or ML systems in production
Strong understanding of LLM design patterns, including prompt engineering, tool calling, structured outputs, and RAG
Strong grasp of clustering, embeddings/NLP, semantic similarity, and anomaly/novelty detection
Experience building classification, ranking, or prioritization systems
Solid background in statistics, experimental design, and model evaluation
Experience with Cradle, Apache Hadoop, and Spark, and with scaling ETL and data pipelines
M.Sc. in CS/EE/Math/Statistics or a related field
Ph.D. is an advantage, not a requirement