Data Scientist - Fraud Analytics

Hybrid in Austin, TX, US • Posted 13 hours ago • Updated 12 hours ago
Contract W2
12 Months
Hybrid
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Fraud
  • Data Scientist
  • Machine Learning
  • ML

Summary

Role: Data Scientist - Fraud Analytics

Location: Austin, TX (100% Onsite)

Experience: 8-12 Years

Job Summary: We are looking for a Lead Data Scientist with strong expertise in Fraud Analytics, Machine Learning, and AI-driven fraud detection solutions.

The ideal candidate will lead end-to-end fraud use case discovery, model development, feature engineering, and deployment of advanced fraud detection systems leveraging ML, Graph Analytics, and AI techniques.

Required Skills

Primary Skills

  • Fraud Analytics
  • Machine Learning
  • Graph Analytics
  • Feature Engineering
  • Dataiku
  • Agentic AI Architecture

Secondary Skills

  • Neo4j
  • Python
  • Model Explainability
  • Statistics
  • Experimentation Frameworks

Key Responsibilities

  • Lead fraud use case discovery and scenario design.
  • Define fraud detection strategies using business rules, Machine Learning, and graph techniques.
  • Identify predictive variables and build fraud feature catalogues.
  • Build and validate pilot fraud detection models.
  • Optimize precision, recall, and fraud catch rates.
  • Collaborate with engineering teams on low-latency scoring architectures.
  • Drive model explainability, governance, and compliance initiatives.
  • Support shadow runs and challenger model validation against existing systems.

Responsibilities Area

  • Data Science & AI Strategy
  • Solution Design & Model Development
  • Fraud Analytics & Investigation Insights
  • Technical Leadership
  • Team Mentoring & Stakeholder Management

Regards,

Gaurav Tripathi

Sr Associate Talent Acquisition

KAnand Corporation

Email:

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 10119616
  • Position Id: GFA7943001
  • Posted 13 hours ago
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