Job Specifications
Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. The Opportunity As a
Data Scientist II for Fraud,
you will be responsible the development of machine learning models that improve USAA's ability to detect and prevent fraud on credit card, debit card, check, deposit, digital payments, as well as in other areas such as claims and disputes. Strong candidates will be able to deploy the following work products and processes: Develop and continuously update internal fraud models in the transactions and payment space, demonstrating techniques ranging from statistics to highly sophisticated AI/ML techniques, to generate highly significant reduction in fraud losses and improvement in Member experience Work with Strategies and Model Management teams to understand and plan model needs Drives continuous innovation in modeling efforts Collaborate with the broader analytics community to share standard methodologies and techniques We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, Phoenix, AZ, Colorado Springs, CO, Charlotte, NC, or Tampa, FL. Relocation assistance is not available for this position. This position can work remotely in the continental U.S. with occasional business travel. What you'll do: Captures, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business. Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs. Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. Composes technical documents for knowledge persistence, risk management, and technical review audiences. Consults with peers for mentorship, as needed. Translates business request(s) into specific analytical questions, completing the analysis and/or modeling, and presenting outcomes to non-technical business colleagues. Consults with Data Engineering, IT, the business, and other internal partners to deploy analytical solutions that are aligned with the customer's vision and specifications and consistent with modeling best practices and model risk management standards. Seeks opportunities and materials to learn new techniques, technologies, and methodologies. Ensures risks associated with business activities are optimally identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree. 2 years of experience in predictive analytics or data analysis OR Advanced Degree (eg, Master's, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field Experience in training and validating statistical, physical, machine learning, and other sophisticated analytics models. Experience in one or more multifaceted scripted language (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models. Ability to write code that is easy to follow, well detailed, and commented where vital to explain logic (high code transparency). Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc. Experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc. Familiarity with performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics. Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc. Ability to communicate analytical and modeling results to non-technical business partners. What sets you apart: US military experience through military service or a militar