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HUMAN's Detection Health and Performance team is seeking a Cyber Detection Analyst to investigate suspicious traffic, identify missed attacks, and reduce false positives while building and evaluating detection logic. You'll combine data analysis, technical troubleshooting, and customer communication to understand detection gaps and share threat insights and recommendations.
In this role, you'll collaborate with Technical Account Managers, Solutions Engineers, Operations, Research, and Engineering teams to maintain strong security outcomes while protecting legitimate users. You'll work throughout the customer lifecycle—from onboarding through ongoing optimization—to help customers maintain effective detection as attacker behavior evolves.
You should have at least 2 years of experience in technical support, security operations, threat analysis, fraud/risk analysis, or data analysis roles, or a relevant degree in cybersecurity or analysis. Strong technical skills are essential: SQL (ideally Snowflake), BigQuery, Datadog, Tableau, Kibana, and log analysis tools. You'll be tested on SQL query writing. Bonus skills include Python, pandas, notebooks, Streamlit, dashboard building, Git, APIs, JSON, and basic scripting.
Knowledge of web architecture (DNS, CDN, GLB, APIs), HTTP protocol, cookies, bots and threat landscape, and basic JavaScript understanding are important. You should be comfortable with statistical analysis of big data and using AI-assisted tools responsibly.
You're analytical, curious, detail-oriented, and able to communicate effectively with both technical and non-technical audiences. You're proactive, adaptable, and thrive in collaborative environments. Fluent English is required.
HUMAN's Human Defense Platform safeguards enterprises from sophisticated bots, fraud, and account abuse, verifying humanity across more than 20 trillion interactions weekly for major global brands. The company offers comprehensive benefits including well-being and learning stipends, flexible work options, and dedicated time off.
