Lead Data Scientist, Fraud Detection Analytics at USAA Colorado Springs, CO

Comunidade Metodista; Colorado Springs (CO); Hybrid; USD 138,000 - 249,000; Full time

Benefits offered by this job

Flexible schedule

Hybrid work setup

Comprehensive benefits

Job summary

USAA seeks a Senior Data Scientist for Fraud Identity Analytics to develop quantitative solutions for detecting identity theft and fraud. You will build ML models, implement graph analytics, and collaborate across teams to deliver production-ready analytical assets.

The role emphasizes communicating results to non-technical partners and driving actionable business outcomes. The position requires extensive data handling skills, mentoring junior staff, and staying current with innovative methods

Qualifications

• Bachelor's degree in a quantitative field or 4 years of related experience.
• 6 years of experience in predictive analytics or data analysis.
• 4 years of experience in training and validating advanced analytics models.
• 4 years of experience using Python or R for statistical analyses and ML models.
• Proven ability to write clear, well-documented code.
• Strong SQL/NoSQL data querying and preprocessing skills.
• Experience in descriptive, diagnostic, and inferential analytics.
• Ability to assess regulatory implications of modeling work.
• Advanced knowledge of supervised modeling concepts and unsupervised techniques.
• Experience mentoring junior data scientists and communicating results to non-technical partners.

Responsibilities

• Gather, interpret, and manipulate structured and unstructured data for advanced analytics.
• Develop scalable, automated ML/optimization solutions for business insights.
• Choose modeling techniques based on data and business needs.
• Create models within MDC and MRM frameworks.
• Draft technical documentation for knowledge preservation and risk management.
• Assess business needs to propose analytics projects with value.
• Collaborate with leaders to prioritize analytical challenges.
• Build a library of production-ready algorithms and code.
• Translate complex requests into analytical questions and actionable recommendations.
• Identify breakthroughs, risks, and challenges for project success.

Job description

Salary: $138,230 - 248,810 per year

Requirements:

• Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or another similar quantitative discipline; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related fields may substitute for the degree requirement.
• 6 years of experience in predictive analytics or data analysis.
• 4 years of experience in training and validating statistical, physical, machine learning, or other advanced analytics models.
• 4 years of experience using a dynamic scripting language (such as Python or R) for statistical analyses and/or developing and scoring AI/ML models.
• Proven track record of writing clear, well-documented code with necessary comments for logic transparency.
• Strong experience in querying and preprocessing data from structured and/or unstructured databases using languages such as SQL, HQL, NoSQL, etc.
• Proficiency in handling structured, semi-structured, and unstructured data files (e.g., delimited numeric data, JSON/XML files, and text documents).
• Demonstrated ability in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• Capability to assess and communicate the regulatory implications and expectations of specific modeling projects.
• Advanced knowledge of classical supervised modeling concepts for prediction (e.g., linear/logistic regression, discriminant analysis, support vector machines).
• Advanced expertise in unsupervised modeling techniques (e.g., k-means clustering, hierarchical clustering, neighbors algorithms).
• Experience in guiding and mentoring junior technical staff in business interactions and model development.
• Experience in communicating analytical results to non-technical business partners with a focus on actionable business recommendations.

Responsibilities:

• Gather, interpret, and manipulate both structured and unstructured data to enable advanced analytical solutions for the business.
• Develop scalable, automated solutions by employing machine learning, simulation, and optimization to deliver valuable business insights.
• Select appropriate modeling techniques considering data limitations, applications, and business needs.
• Create and implement models within the Model Development Control (MDC) and Model Risk Management (MRM) frameworks.
• Draft and assist peers in creating technical documents for knowledge preservation, risk management, and technical review purposes.
• Assess business needs to recommend analytical and modeling projects that can add value.
• Collaborate with business and analytics leaders to prioritize analytical and modeling challenges.
• Build and maintain a robust library of reusable, production-quality algorithms and supporting code to ensure transparency and the use of high-quality data in development and research efforts.
• Translate complex business requests into specific analytical questions, perform the analysis or modeling, and communicate outcomes to non-technical colleagues with a focus on actionable recommendations.
• Identify project breakthroughs, risks, and challenges that may impede project success or implementation.
• Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets in line with modeling best practices and model risk management standards.
• Stay updated on innovative techniques and actively seek learning opportunities to expand knowledge of new methodologies.
• Mentor junior data scientists in modeling, analytics, and computer science tasks.
• Participate in internal communities that promote the development and transformation of data science technologies and culture.
• Ensure risks associated with business activities are optimally identified, measured, supervised, and controlled according to risk and compliance policies and procedures.

Technologies:

• AI
• Support
• JSON
• Machine Learning
• NoSQL
• Phoenix
• Python
• SQL
• XML
• Graph Database
• Security

More:

At USAA, our mission is to empower our members to achieve financial stability through highly competitive products, exceptional service, and trusted advice. We aspire to be the top choice for the military community and their families. We offer a rewarding career where our core values—honesty, integrity, loyalty, and service—guide our interactions with each other and our members.

We provide a flexible work environment, requiring office attendance four days a week, with positions available in San Antonio, TX; Plano, TX; Phoenix, AZ; Colorado Springs, CO; Charlotte, NC; or Tampa, FL. Please note that relocation assistance is not available for this role. The Senior Data Scientist - Fraud Identity Analytics will focus on developing and implementing quantitative solutions to enhance our ability to detect and prevent identity theft, account takeovers, and various fraud types. You will be involved in creating machine learning models and implementing broad solutions, such as graph analytics, to safeguard USAA and our members from emerging threats. Strong candidates will actively collaborate across teams and work with a variety of tools and data to drive impactful outcomes. Compensation for this position ranges from $138,230 to $248,810, depending on experience and market data. Additionally, we offer a comprehensive benefits package to support our employees' physical, financial, and emotional well-being. Our employees can enjoy extensive medical, dental, and vision coverage, 401(k), pension, life insurance, and generous paid time off, including holidays and volunteer hours. We value diversity and encourage anyone meeting the qualifications to apply for an opportunity to be part of our team. USAA is an Equal Opportunity Employer, committed to employing individuals regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.