5-8 years in fraud, trust & safety, or risk-related fields working directly with fraud data
Proficient in SQL and Python for hands-on data analysis
Strong understanding of machine learning concepts relevant to fraud detection
Experience analyzing large-scale datasets for pattern identification
Ability to communicate technical findings effectively to diverse audiences
Customer-facing experience with an understanding of varied business priorities
Responsibilities
Collaborate with Trust and Safety and Data Science teams to identify and address fraud patterns
Transform detection findings into actionable signals and decisioning logic
Engage with various clients to tailor risk management strategies
Develop dashboards and optimize models for customer solutions
Investigate false positives and vulnerabilities by analyzing raw data
Lead investigations into fraud incidents and provide remediation recommendations
Advocate for product opportunities based on systemic issues identified
Benefits
Flexible work arrangements including remote opportunities
Comprehensive health and wellness programs
Professional development and training support
Collaborative and innovative work environment
Access to advanced analytics and fraud detection tools
Full Job Description
What you'll do:
Work with our Trust and Safety Architect and Data Science teams to surface emerging fraud patterns across the network escalate and proactively take them down.
Detect patterns and turn those findings into sharper signals, tighter configurations, and smarter decisioning logic.
Work across different verticals and closely with customers, partners and prospects with different risk appetites - some optimizing for approval rates, some minimizing chargebacks, some fighting account takeover and other types of abuse.
Help build dashboards, tune models, decision logic and custom signals to help customers achieve their desired business outcomes
Identify sources of false positives, possible coverage gaps and other vulnerabilities by digging into raw event streams; form a hypothesis, design a test and implement the fix
Lead forensic investigations during fraud spikes: trace attack patterns to their source, identify the technique being used, deliver a clear writeup with remediation steps
Distinguish between one-off anomalies and systemic gaps that indicate a product opportunity - and advocate for the latter with rigor
Contribute to detection frameworks, investigative tooling, and internal playbooks that make every engineer and analyst at Sift more effective
Be the conduit between customer reality and internal roadmap; your field observations should directly accelerate what Sift ships next
What We're Looking For
Required
5-8 years in fraud, trust & safety, risk, or a closely related technical domain - you've spent meaningful time working with fraud data, not just adjacent to it
Strong SQL and Python skills; you reach for code to answer a question, not to build a pipeline
Strong understanding of ML concepts applied to fraud: classification models, feature engineering, precision/recall tradeoffs, threshold calibration, score drift
Experience analyzing large-scale behavioral or transactional datasets to find patterns and anomalies - you know what a fraud ring looks like in the data, not just in a textbook
Ability to communicate technical findings to both technical and non-technical stakeholders; you can write a forensic investigation report and present it to a VP of Risk in the same week
Customer-facing experience; you understand that different businesses have different priorities, and that listening before optimizing is part of the job
Nice to Have
Hands-on experience with fraud detection platforms (in house or 3rd party)
Hands-on experience building with AI: LLM APIs, prompt engineering, or agentic workflows - whether that's automating an investigation step, building a tool that surfaces patterns from raw data, or wiring together a multi-step agent to accelerate fraud analysis
Familiarity with real-time event processing systems
Experience with rules-based decisioning systems alongside ML - knowing when a hard rule beats a model score
Background in payments, e-commerce, fintech, marketplace, or account security fraud
Prior forward deployed, staff engineering, or embedded consulting experience at a technical product company
Computer Science, Mathematics, Statistics, Information Systems, Economics degree or equivalent
