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Mid-level, metro location, popular data role with common tech requirements and recognizable brand increases applicant competition.
Core analytics skills transferable but domain-specific fraud and integrity expertise raises medium sensitivity.
Explicit 4+ years, mandatory SQL/Python/Spark and AI/ML proficiency make filters relatively strict.
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Own end-to-end complex analytical problems in platform integrity across 1-3 Problem Space Teams (PSTs).
Develop and implement real-time risk detection measures leveraging advanced AI and system frameworks to identify fraud, safety, and identity abuse threats.
Use feature engineering and synthetic data simulation to build robust input models and dynamic risk mitigation tools while mentoring junior analysts and collaborating with mid-to-senior stakeholders.
4+ years of experience in data-related or quantitative fields.
Bachelor's Degree in Analytics, Statistics, Computer Science, or Engineering.
Proficient in SQL, Python, Spark, and statistical/experimentation techniques.
Strong understanding of AI/ML, feature engineering, and system-thinking applied to complex risk ecosystems.
Experienced in independently solving complex analytical problems with a focus on platform integrity and risk detection.
Able to transition workflows from manual analysis to AI-enhanced, agentic system configurations.
Skilled at collaborating cross-functionally and mentoring junior analysts within high-impact operational environments.