# Staff Data Scientist

**Company:** [Sift](https://hotfix.jobs/companies/sift)
**Location:** Remote
**Role:** Data Science
**Salary:** $195k – $265k/yr
**Experience:** 5+ years
**Skills:** Xgboost, Lightgbm, Cnns, Rnns, Transformers, Gnns, Graph Neural Networks, Machine Learning, Statistical Modeling, Fraud Detection
**Posted:** 2026-06-08

> Staff Data Scientist owning advanced ML modeling strategies for fraud detection across payment fraud, account takeover, and identity abuse. Requires 5+ years production modeling experience, deep fraud/security domain expertise, and mastery of tree-based, deep learning, and graph methods.

## Job Description

## What you'll do

- Architect and own advanced modeling strategies across fraud and abuse problem domains (payment fraud, account takeover, identity spoofing, account abuse, content manipulation, credential stuffing)
- Drive framework selection—deciding when gradient boosting on velocity features suffices, when graph neural networks unlock network effects, when deep learning on sequence data catches adaptive fraud patterns
- Work backward from business metrics (customer adoption, chargeback reduction, operational lift) to model objectives informed by threat models
- Establish and defend model quality standards that account for adversarial dynamics
- Develop diagnostic frameworks to decompose model performance by fraud type, attacker sophistication level, geography, and temporal patterns
- Own the post-launch monitoring process, identify when degradation signals retrain vs. architecture change vs. active evasion by fraud rings
- Design sampling strategies that catch emerging fraud patterns before they scale
- Lead statistical innovation on highest-leverage fraud problems
- Explore novel feature representations drawn from understanding of fraud mechanics (network propagation of compromised accounts, timing signatures of automated attacks, behavioral deviation from account history)
- Run rigorous experiments to validate whether a suspected fraud pattern is exploitable or a false lead
- Publish findings internally (and externally where disclosable), and mentor junior data scientists
- Partner with ML engineering and information security on adversarial robustness
- Co-design models that resist manipulation; pressure-test feature importance against known evasion tactics
- Own the handoff from research to serving
- Build automated workflows that scale human expertise while respecting fraud complexity
- Leverage AI-assisted tools (LLMs, AutoML frameworks) to accelerate experimentation while maintaining verification checkpoints informed by domain knowledge

## What will make you a strong fit

- Deep, hands-on knowledge of fraud and information security patterns
- Modeled payment fraud, account takeover, identity abuse, or network attacks in production
- Understand attacker incentives, exploit chains, evasion tactics, and how fraud patterns evolve in response to defenses
- 5+ years of hands-on modeling experience with production accountability
- Shipped models to millions of users, owned their performance in production
- Deep expertise in multiple modeling paradigms: Tree-based methods (XGBoost, LightGBM), deep learning architectures (CNNs, RNNs, transformers for sequential/graph data), and graph-based methods (GNNs, message passing, network propagation)
- Advanced degree in Statistics, Data Science, Machine Learning, or equivalent (MS or PhD in quantitative field, or 8+ years of demonstrable statistical modeling depth in production fraud/security contexts)
- Lean, deep statistical intuition informed by domain reality
- Proven ability to partner with AI-assisted automation tools (LLMs, AutoML)
- Comfort working in ambiguity and adversarial contexts

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