Job Description

Salary: $93,770 - 168,790 per year Requirements:

• Bachelors degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or a related quantitative field; OR 4 years of relevant experience in statistics, mathematics, or quantitative analytics may substitute for a degree.
• Minimum of 2 years of experience in predictive analytics or data analysis, OR an advanced degree (e.g., Masters, PhD) in a quantitative discipline.
• Proven experience in training and validating statistical, physical, machine learning, and other advanced analytical models.
• Proficiency in one or more programming languages (e.g., Python, R) for statistical analysis and building AI/ML models.
• Strong coding skills with clear documentation to ensure high code transparency.
• Experience in querying and preprocessing data from various database formats using SQL, HQL, NoSQL, or similar.
• Familiarity with handling structured, semi-structured, and unstructured data types like numeric, JSON/XML files, and text documents.
• Understanding of performing ad-hoc analytics using descriptive and inferential statistics.
• Knowledge of classical supervised modeling techniques such as linear/logistic regression, support vector machines, and decision trees.
• Understanding of unsupervised modeling concepts like k-means clustering and hierarchical clustering.
• Strong communication skills to present analytical findings to non-technical business stakeholders.

• Develop and enhance machine learning models to improve fraud detection and prevention across various payment methods.
• Collaborate with Strategies and Model Management teams to identify model requirements and drive innovation in modeling approaches.
• Capture, interpret, and manipulate both structured and unstructured data to support analytical solutions.
• Select suitable modeling techniques that align with business needs and data constraints.
• Develop and implement models following the Model Development Control and Model Risk Management frameworks.
• Create technical documentation for knowledge sharing and risk management purposes.
• Work with Data Engineering, IT, and internal partners to deploy analytical solutions that meet customer specifications and adhere to best practices.
• Seek opportunities to learn new techniques and methodologies in the analytics field.
• Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled according to compliance standards.

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

More:

At USAA, we are dedicated to empowering our members to attain financial security through our competitive products and exceptional service. We strive to be the first choice for the military community and their families. Joining us as a Data Scientist II for Fraud means being a part of a culture founded on our core values of honesty, integrity, loyalty, and service. We offer a flexible work environment, requiring four days in the office, with the option to work remotely within the continental U.S. We provide comprehensive benefits to support your physical, financial, and emotional wellness, including medical and dental plans, paid time off, and professional development opportunities. Our commitment to our employees reflects our recognition as an Equal Opportunity Employer.

last updated 51 week of 2025

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