Principal Applied Scientist
Core
Identify emerging fraud tactics and adversary infrastructure across monetization flows and LLM-native systems through threat modeling and big data analysis.
Role type
Principal Applied Scientist (Fraud Intelligence & Adversarial Simulation)
Builds
Test harnesses, monitored indicators, reproducible regression scenarios, and shared investigation infrastructure for defense teams.
Domain
Monetization fraud, advertising ecosystems, and LLM-native security
Deliverable
production ML models | dashboards & analysis | research
Required skills
threat modeling, graph-based analysis, temporal analysis, entity resolution, adversarial simulation design, statistical detection methods, large-scale telemetry analysis, attack chain reconstruction, hypothesis-driven investigation, technical writing for executive audiences, machine learning evaluation (precision/recall/drift), Python, Spark, SQL
Preferred skills
experience with invalid traffic detection, OpenRTB mechanics, botnets, device spoofing, residential proxy networks, synthetic identity abuse, red teaming, FMEA, STPA, agent-assisted analysis tooling
Technologies
Python, Spark, SQL
Responsibilities
Identify emerging fraud tactics and adversary infrastructure; assess high-risk assumptions and attack paths via structured modeling; design safe adversarial simulations; establish authoritative ground truth by correlating evidence; develop shared investigation infrastructure and reusable libraries; lead end-to-end reconstruction of material incidents; shape scientific and technical strategy for fraud intelligence.
Seniority
Principal, strategy & mentorship
