Sift
Forward Deployed Engineer, Trust and Safety
Remote - USA
Sponsorship not specifiedDetected 22 days ago
PythonSQLMachine LearningData ScienceLLMsStatisticsCustomer Success
About the role
- We're people that are passionate about making the internet a safer and more trusted place for all.
- 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.
Responsibilities
- 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
- At Sift, we are intentionally building a diverse, equitable, and inclusive workplace.
- This document provides transparency around how
Requirements
- 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 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
- Ability to travel up to 30%
Nice to have
- Hands-on experience with fraud detection platforms (in house or 3rd party)
- 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
- Sift is the AI-powered fraud platform securing digital trust for leading global businesses.
- Visit us at sift.com http://sift.com and follow us on LinkedIn https://www.globenewswire.com/Tracker?data=XHeK0v8NcNrEkwcDe8QxwpZeCkdQqNyKlni83U-CUmrprdKXWpVlYOAbVzwe2OmlwIUN-q4HXk4hf_dazpHx2NMM1CW_SYj740q9mxXNQI4=.
Skills
- Distinguish between one-off anomalies and systemic gaps that indicate a product opportunity - and advocate for the latter with rigor
- Be the conduit between customer reality and internal roadmap; your field observations should directly accelerate what Sift ships next
Compensation
- 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.
Benefits
- We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need.
- Identify sources of false positives, possible coverage gaps and other vulnerabilities by digging into raw event streams
Company info
- 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.
- 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.
- You've helped build tools, models and detection platforms at companies that have had to work through these threats at a global level.
- 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
This listing is sourced directly from Sift's careers page and normalized into a canonical job model.