# Forward Deployed Data Scientist

**Company:** [Sift](https://hotfix.jobs/companies/sift)
**Location:** Seattle, WA, San Francisco, CA
**Role:** Data Science
**Salary:** $170k – $230k/yr
**Experience:** 5+ years
**Skills:** SQL, Python, Machine Learning, Fraud Detection, Feature Engineering, Precision/Recall, Threshold Calibration, Score Drift, Behavioral Data Analysis, Transactional Data Analysis, LLM APIs, Prompt Engineering, Real-Time Event Processing, Rules-Based Decisioning
**Posted:** 2026-06-29

> Forward Deployed Data Scientist partnering with customers to detect emerging fraud patterns, tune ML models and signals, conduct forensic investigations, and translate field insights into product improvements for Sift's AI-powered fraud platform. Requires 5-8 years in fraud/trust & safety with strong SQL, Python, and ML expertise.

## Job Description

## What you'll do
- Work with 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.
- 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.

## 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 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, Mathematics, Statistics, Information Systems, Economics degree or equivalent.

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