Lead Graph Data Scientist, Identity Analytics

USAA

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
San Antonio, TXPhoenix, AZTampa, FLCharlotte, NCColorado Springs, CO
Salary
$164,780–$314,960 / yr
Posted
8 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $166k
This role $240k
$99k most similar roles pay here $338k

This role pays more than 90% of similar roles. Most pay $127,862–$204,400 — the shaded band above. At the midpoint, this role pays about $240k versus about $166k for comparable roles.

Based on 240 similar postings.

Employer

About USAA

USAA (United Services Automobile Association) is a San Antonio-based Fortune 500 financial services company founded in 1922, dedicated to providing insurance, banking, and investment solutions exclusively to U.S. military members, veterans, and their families.

USAA currently has 14 open roles on FindRole.

Listed pay typically runs $143,320–$273,930 across 14 roles with salary data.

Most-posted roles

View all roles at USAA

At a glance

TL;DR · Lead Graph Data Scientist, Identity Analytics

The Lead Graph Data Scientist - Identity Analytics joins the fraud team to develop and implement quantitative solutions aimed at detecting and preventing identity theft, account takeover, and synthetic fraud. This role involves building machine learning models, deploying graph analytics capabilities, and identifying criminal networks through graph databases and techniques like graph neural networks. The candidate will manage a project portfolio, mentor junior staff, and collaborate with technology partners to integrate new data sources into production-ready systems. Key technical requirements include proficiency in Python or R, SQL, and NoSQL for querying structured and unstructured data. Expertise is required in supervised learning methods such as Random Forests and XGBoost, as well as unsupervised techniques like k-means clustering. The role focuses on solving complex financial crime problems to mitigate losses and improve the member experience through advanced fraud detection algorithms.

What you'll do

  • Develop and update machine learning models to detect and prevent identity theft, account takeover, and synthetic fraud.
  • Deploy graph database technologies and techniques to identify criminal networks and mitigate financial crime risks.
  • Integrate new data sources into existing models and graphs to enhance predictive power and business performance.
  • Build and maintain a library of reusable, production-quality algorithms and code for transparent model development.
  • Translate complex business requirements into specific analytical questions and communicate results to non-technical stakeholders.
  • Manage project portfolios by tracking milestones, identifying risks, and ensuring compliance with risk management frameworks.
  • Mentor junior data scientists in modeling, analytics, and technical communication skills.
  • Drive innovation in modeling efforts using advanced techniques such as graph neural networks.

What we're looking for

  • Bachelor's degree in a quantitative field or 4 years of experience in statistics, mathematics, or quantitative analytics.
  • 8 years of experience in predictive analytics or data analysis.
  • 6 years of experience training and validating statistical, physical, machine learning, and other advanced analytics models.
  • 4 years of experience in dynamic scripted languages like Python or R for building and scoring AI/ML models.
  • Expert knowledge of supervised modeling (e.g., Random Forests, XGBoost) and unsupervised modeling (e.g., k-means clustering).
  • Strong experience querying and preprocessing data from structured and unstructured databases using SQL or NoSQL.
  • Over 4 years of experience with graph databases and graph solutions.
  • Experience in fraud or financial crimes model development.

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