Jobs at Sift - RemoteWorkUSA

About the Team:We’re people that are passionate about making the internet a safer and more trusted place for all. We love the fraud and trust & safety space and want to teach companies how they can protect themselves, their users and create frictionless experiences for legitimate consumers. As a Forward Deployed Engineer, Trust and Safety, you are heavily experienced in detecting and acting on multiple types of online abuse from a technical and quantitative perspective. You’ve helped build tools, models and detection platforms at companies that have had to work through these threats at a global level.What you’ll do:<ul><li>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.</li><li>Detect patterns and turn those findings into sharper signals, tighter configurations, and smarter decisioning logic.</li><li>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.</li><li>Help build dashboards, tune and build models, decision logic and custom signals to help customers achieve their desired business outcomes</li><li>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</li><li>Lead forensic investigations during fraud spikes: trace attack patterns to their source, identify the technique being used, deliver a clear writeup with remediation steps</li><li>Distinguish between one-off anomalies and systemic gaps that indicate a product opportunity - and advocate for the latter with rigor</li><li>Contribute to detection frameworks, investigative tooling, and internal playbooks that make every engineer and analyst at Sift more effective</li><li>Be the conduit between customer reality and internal roadmap; your field observations should directly accelerate what Sift ships next</li><li>Some travel may be required</li></ul><h2>What We're Looking For</h2>Required<ul><li>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</li><li>Strong SQL and Python skills; you reach for code to answer a question, not to build a pipeline</li><li>Strong understanding of ML concepts applied to fraud: classification models, feature engineering, precision/recall tradeoffs, threshold calibration, score drift</li><li>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</li><li>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</li><li>Customer-facing experience; you understand that different businesses have different priorities, and that listening before optimizing is part of the job</li></ul>Nice to Have<ul><li>Hands-on experience with fraud detection platforms (in house or 3rd party)</li><li>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</li><li>Familiarity with real-time event processing systems</li><li>Experience with rules-based decisioning systems alongside ML - knowing when a hard rule beats a model score</li><li>Background in payments, e-commerce, fintech, marketplace, or account security fraud</li><li>Prior forward deployed, staff engineering, or embedded consulting experience at a technical product company</li><li>Computer Science, Data Science, Mathematics, Statistics, Information Systems, Economics degree or equivalent</li></ul>Let’s build it together:At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.This document provides transparency around how Sift handles the personal data of job applicants: <a href='https://sift.com/recruitment-privacy'>https://sift.com/recruitment-privacy</a> A little about us: Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at <a href='http://sift.com'>sift.com</a> and follow us on <a href='https://www.globenewswire.com/Tracker?data=XHeK0v8NcNrEkwcDe8QxwpZeCkdQqNyKlni83U-CUmrprdKXWpVlYOAbVzwe2OmlwIUN-q4HXk4hf_dazpHx2NMM1CW_SYj740q9mxXNQI4='>LinkedIn</a>.

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Engineering