Job Description
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Company Overview
A leading technology company focused on trust and safety helps businesses create safer and more trusted digital experiences. Its technology enables organizations to detect fraud, prevent online abuse, protect users, and create frictionless experiences for legitimate consumers.
Key Responsibilities
- Work with Trust and Safety Architects and Data Science teams to identify emerging fraud patterns across the network and proactively address threats.
- Detect behavioral and transactional patterns and translate findings into stronger signals, improved configurations, and more effective decisioning logic.
- Work across different industry verticals and collaborate closely with customers, partners, and prospects with varying risk priorities.
- Develop dashboards, tune and build models, decision logic, and custom signals to help customers achieve their desired business outcomes.
- Identify sources of false positives, coverage gaps, and other vulnerabilities by analyzing raw event streams.
- Form hypotheses, design tests, analyze results, and implement solutions to address identified vulnerabilities.
- Lead forensic investigations during fraud spikes by tracing attack patterns to their source and identifying techniques used by attackers.
- Produce clear investigation reports with actionable remediation recommendations.
- Distinguish between isolated anomalies and systemic gaps that may represent opportunities for product improvements.
- Contribute to fraud detection frameworks, investigative tools, and internal playbooks.
- Share field observations and customer insights to help inform and accelerate future product development.
- Collaborate with technical and non-technical stakeholders to communicate findings and recommended actions.
- Travel periodically as required by the role.
Required Qualifications
- 5–8 years of experience in fraud, trust and safety, risk, or a closely related data science field.
- Significant experience working directly with fraud-related data.
- Strong SQL and Python skills with the ability to use code to investigate and answer complex questions.
- Strong understanding of machine learning concepts applied to fraud detection, including classification models, feature engineering, precision/recall tradeoffs, threshold calibration, and score drift.
- Experience analyzing large-scale behavioral or transactional datasets to identify patterns, anomalies, and potential fraud activity.
- Ability to recognize fraud patterns and investigate complex behavioral signals using data.
- Strong written and verbal communication skills with the ability to explain technical findings to both technical and non-technical stakeholders.
- Ability to produce detailed forensic investigation reports and present findings to senior risk and business leaders.
- Customer-facing experience and an understanding of how business priorities can vary across different organizations.
- Strong problem-solving skills and the ability to balance analytical findings with customer-specific objectives.
Preferred Qualifications
- Hands-on experience with fraud detection platforms, whether internally developed or third-party solutions.
- Experience building AI-powered solutions using LLM APIs, prompt engineering, or agentic workflows.
- Experience applying AI to automate investigation processes, identify patterns in raw data, or accelerate fraud analysis.
- Familiarity with real-time event processing systems.
- Experience working with rules-based decisioning systems alongside machine learning models.
- Background in payments, e-commerce, fintech, marketplaces, or account security fraud.
- Previous experience in forward-deployed engineering, staff engineering, or embedded consulting at a technical product company.
- Degree in Computer Science, Data Science, Mathematics, Statistics, Information Systems, Economics, or a related field, or equivalent practical experience.
Compensation
The anticipated starting on-target earnings range for this role is $170K – $230K. The position may also be eligible for equity and other benefits. Compensation may vary based on factors including level, location, transferable skills, work experience, business needs, and market conditions. The compensation range and final offer may be adjusted in the future.
Disclaimer
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