Detection Researcher (Data Science)

Description

Dream is a pioneering AI cybersecurity company delivering revolutionary defense through artificial intelligence. Our proprietary AI platform creates a unified security system safeguarding assets against existing and emerging generative cyber threats. Dream's advanced AI automates discovery, calculates risks, performs real-time threat detection, and plans an automated response. With a core focus on the "unknowns," our AI transforms data into clear threat narratives and actionable defense strategies.

Dream's AI cybersecurity platform represents a paradigm shift in cyber defense, employing a novel, multi-layered approach across all organizational networks in real-time. At the core of our solution is Dream's proprietary Cyber Language Model, a groundbreaking innovation that provides real-time, contextualized intelligence for comprehensive, actionable insights into any cyber-related query or threat scenario.

We are seeking a skilled and motivated Data Scientist, specializing as a Detection Researcher to join our core AI research team. In this role, you will help design, develop, and optimize advanced anomaly detection systems to identify sophisticated cyber threats and behavioral deviations across networks and systems. This position involves hands-on research at the intersection of cybersecurity and machine learning, driving novel approaches for detecting unknown and emerging threats in real-time.

Responsibilities

• Research, design, and prototype innovative machine learning models, including anomaly detection techniques and other unsupervised methods, tailored for cybersecurity use cases.
• Work with domain experts specializing in cyber threat behaviors to guide the development of statistical and machine learning-based anomaly detection models.
• Collaborate with cybersecurity experts and MLOps / Engineers to integrate machine learning capabilities, focusing on anomaly detection, into Dream’s platform.
• Create and validate robust evaluation methods to assess models’ performance, coverage, and false positive rates.
• Continuously monitor the latest research and advancements in machine learning and anomaly detection methods to inform Dream's roadmap.
• Contribute to the development of scalable frameworks for real-time anomaly detection in production environments.

Responsibilities

Requirements

Skills

• B.Sc. in Computer Science, Mathematics, Statistics, or a related field.
• 5+ years of experience in data science, or machine learning focused on anomaly detection.
• Experience with time-series analysis, unsupervised learning, and statistical modeling techniques.
• Strong programming skills (Python, or similar) and experience with ML frameworks (e.g. PyTorch, TensorFlow).
• Proven Experience designing and implementing machine learning algorithms and successfully deploying them to production.
• Excellent communication and teamwork skills.
• Fluency in English.

Preferred Qualifications

• M.Sc. (or PhD) in Computer Science, Mathematics, Statistics, or a related field.
• Experience with streaming data architectures and real-time detection systems.

·      Background in behavioral analytics, graph-based anomaly detection, or adversarial modeling.

• Deep understanding of network behaviors, threat patterns, and attack vectors.
• Familiarity with Cyber security, in particular data, such as logs, flows, and endpoint data.
• Knowledge of large-scale distributed systems for cybersecurity data processing.
• Interest in combining anomaly detection techniques with LLM-driven contextual analysis.