Forward Deployed Data Scientist

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 and build 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.
• Some travel may be required.

Requirements

• 5 - 8 years in fraud, trust & safety, risk, or a closely related data science 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, Data Science, Mathematics, Statistics, Information Systems, Economics degree or equivalent.