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Strong regional brand, metro location, generalist Data Analyst title, and common SQL/Python skills increase applicant competition.
Core SQL/Python and analytics skills are transferable, though risk domain knowledge increases specificity.
Explicit 1+ years requirement plus mandatory SQL, Python, and degree raises screening rigidity.
Analyse user behaviour and product funnels to identify potential risks across multiple risk domains like account security, user safety, and content integrity.
Use GenAI tools and auto-adaptive risk frameworks to automate metric reviews, refine safety and risk rules, and support anomaly detection systems.
Contribute to data reliability through metric onboarding, data pipeline cleanups, and improving architectural efficiency for risk detection.
1+ years of experience in data-related or quantitative fields.
Bachelor's Degree in Analytics, Statistics, Computer Science, or related field.
Proficiency in SQL, Python, and data visualisation tools such as Power BI or Tableau.
Fundamental awareness of AI/ML concepts and system-thinking; understanding of basic statistical techniques and experimentation.
Comfortable working with evolving AI and auto-adaptive tools to enhance analytical workflows in a risk and safety context.
Able to independently deliver insights to measure and reduce risk while managing analytical ambiguity with some guidance.
Experienced in collaborating with partners to develop data-driven rules targeting fraud, safety, or integrity issues.