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:
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
What We're Looking For
Required
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
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: https://sift.com/recruitment-privacy
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 sift.com and follow us on LinkedIn.