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Senior Data Scientist (Fraud Detection)

DataVisor

Skills

Required

  • fraud detection
  • cybersecurity
  • data tools
  • Spark
  • Hadoop
  • cloud platforms
  • AWS
  • GCP
  • Azure
  • Python
  • SQL
  • PySpark

Preferred

  • machine learning models
  • Logistic
  • Regression
  • Gradient Boosting
  • ideation
  • prototyping
  • device intelligence
  • network graphs
  • transaction records

Join our Fraud Detection team as a Senior Data Scientist. In this dual-track role, you will develop machine learning systems to catch fraud at scale and lead investigations into specific fraud attacks. You will work with large-scale data, develop predictive features, and produce technical reports for various stakeholders. This is an opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud.

Responsibilities

  • Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring.
  • Develop highly predictive features from complex, large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records.
  • Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces. Reconstruct attacker sequences and hypothesize actor intent and tooling.
  • Investigator mindset: demonstrated skill in pattern synthesis, hypothesis testing, and triaging signal from noise in ambiguous, adversarial cases — not just building and monitoring models

Requirements

  • 3+ years of applied experience in fraud detection, cybersecurity, or a related adversarial/high-velocity risk domain (fintech, consumer payments, banking, SaaS, marketplace risk, or security research)
  • Experience with large-scale data tools (Spark, Hadoop, etc.) and cloud platforms (AWS, GCP, Azure)
  • Professional proficiency in written and spoken English, with the ability to collaborate effectively in a global, cross-functional team
  • Hands-on experience with the machine learning lifecycle in a production environment
  • Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field
  • Excellent communication skills — able to explain complex, ambiguous, or technical behavior clearly to both technical and non-technical audiences, including customers and executives
  • Strong programming skills in Python (must-have) and proficiency with SQL; experience with PySpark is a significant plus

Preferred

  • Solid understanding of both classic machine learning models (Logistic Regression, Gradient Boosting, etc.)