Sift's fraud prevention platform stops payment fraud and account takeover in real time - with transparent workflows, global data and expert analysts. Backed by Insight.
About the role
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.
What they're looking for
- 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
More about this role
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.
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...
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