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Job Title: Risk Analyst Location: Remote (Worldwide)Job Summary: The Risk Analystis responsible for identifying, assessing, and mitigating operational, financial, regulatory, and reputational risks that could impact organizational performance. This role analyzes data, processes, and control environments to detect vulnerabilities, evaluate exposure levels, and recommend effective risk mitigation strategies. Responsibilities Proactively monitor platform data to identify emerging risks, including payment fraud, account takeovers, spam campaigns, phishing scams, and synthetic identity networks. Use SQL, Python, or analytics to query large datasets, uncover attack vectors, and quantify the scale and impact of abusive behavior. Design and implement rules, models, and workflows to detect and prevent fraudulent or high-risk activity with minimal friction for legitimate users. Partner with Engineering, Product, Finance, and Legal teams to build risk controls into the product development lifecycle and ensure compliance with financial regulations. Serve as a key responder during active risks events (e. g., large-scale fraud spikes, payment processor threats). Make rapid decisions to contain losses while maintaining user trust. Build dashboards and reports to track key risk indicators (KRIs), communicate findings to leadership, and measure the effectiveness of mitigation strategies. Provide feedback on internal risk tools and collaborate with engineers to improve detection capabilities and automate manual reviews. Assist in evaluating and integrating third-party risk intelligence vendors (e. g., threat intelligence feeds, identify verification services). Requirements Minimum of 2 years of experience in Risk Analysis, Fraud Intelligence, Trust & Safety Analytics, or Financial Crime investigations within the tech, e-commerce, or fintech sectors. Strong SQL skills are essential. Experience with Python, R, or data visualization tools (Tableau, Looker). Ability to translate raw data into actionable insights. Comfortable working with large, messy datasets to find signals of abuse. Deep understanding of common attack vectors (e. g., chargeback fraud, promo abuse, botnets, deepfakes) and risk mitigation strategies (e. g., rule-based detection, machine learning models). Ability to make quick, high-stakes decisions during active incidents, balancing loss prevention with user experience. Strong ability to present complex technical findings to non-technical stakeholders, including executives and legal teams. Familiarity with global risk and compliance frameworks such as AML (Anti-Moey Laundering), KYC (Know Your Customer), GDPR, or PSD 2 (Payment Services Directive). Proactive curiosity and willingness to dig deeper than surface-level metrics to uncover hidden abuse patterns. Ability to lead cross-functional initiatives and manage priorities in a fast-paced environment.
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