skills and experience building research tools, automation, data analysis workflows, detection prototypes, or feature engineering pipelines. Experience analyzing messy real-world data, investigating anomalies, validating hypotheses, and drawing practical conclusions from incomplete information. Familiarity with machine learning training and validation concepts, such as train/test split, validation sets, overfitting, leakage, feature quality, precision/recall, false positives, false negatives, and model evaluation. Ability to produce data features in a structured, reliable, and model-friendly way. Ability to think like an attacker while designing reliable, scalable, and explainable defenses. Strong problem-solving skills, independence, persistence, and a “getting things done” attitude. Excellent communication and interpersonal skills. Ability to work closely with engineering, product, and data science teams and translate research insights into practical product capabilities. #LI-AM1 #LI-Hybrid #LI-TL1 #LI-Hybrid
